Community data aggregation with automated followup

ABSTRACT

A system and method are disclosed for the collection and aggregation of data from contributing members of a community, such as health-related, personal, genomic, medical, and other data of interest for individuals and populations. Contributors become members of a community upon creation of an account and providing of data or files. The data is received and processed, such as to analyze, structure, perform quality control, and curate the data. Value or shares in one or more community databases are computed and attributed to each contributing member. The data is controlled to avoid identification or personalization. Steps are taken to determine incompleteness and incorrectness of the data, and the data may be improved or completed automatically, based upon interaction with members, additional contributions of data, and so forth.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/848,250, entitled “COMMUNITY DATA AGGREGATIONWITH AUTOMATED FOLLOWUP,” filed on Apr. 14, 2020, which is acontinuation of and claims priority to U.S. patent application Ser. No.16/399,342, entitled “COMMUNITY DATA AGGREGATION WITH AUTOMATEDFOLLOWUP,” filed on Apr. 30, 2019, which claims priority to PCTApplication No. PCT/US2018/067933, entitled “COMMUNITY DATA AGGREGATION,COMPLETION, CORRECTION AND USE”, filed on Dec. 28, 2018, each of whichare hereby incorporated by reference in their entireties for allpurposes.

BACKGROUND

The present invention relates to techniques for filling important gapsin data, particularly in health-related data, including electronichealth record (EHR) data and patient reported outcomes (PRO) data, butalso in a wide range of data types, such as omic data, personal data,demographic data, and so forth. The invention also relates to platformsand approaches to aggregating data from contributing members of acommunity, compensating or motivating contributing members, andutilizing the data to aid both members and a larger community.

The value of big data has become generally accepted, creating anindustry drive to aggregate different types of data in order to usemachine learning and other tools to drive discovery. At scale even weakdata can be valuable in unlocking links between our health and ourgenomic information and our daily habits, such as diet, exercise,drinking, smoking, hours of sleep, etc. Therefore, it is valuable tocollect an individual's data from health institutions and through directsurveys and interviews. Additionally, it is now possible to collectimplicit data through grocery store loyal customer tracking of purchaserecords, credit card records, online search habits, etc.

In all cases, even though at an aggregate level the data is valuable,there is a significant amount of missing information. Some examples areEHR and PRO data. EHRs are typically filled out by doctors online whilemeeting with patients. There are at least two types of challenges withthese records. First, EHRs are designed primarily as billing systems formedical institutions. They record patient information such as testresults, symptoms, and doctors' observations and recommendations,including treatment prescriptions. They do not typically record outcomesafter treatment or whether patients followed the prescribed treatmentregime. Moreover, doctors and other health professionals do not alwaysuse digital entry fields to record information. They often simply writeinformation in unstructured comment fields. Additionally, thenomenclature and ontology used when inputting data is not standardized.There is a major challenge associated with deciphering and decryptingthese free form comments.

In the case of PRO records, often patient outcomes are never reported ina system. There is no current method for identifying even when a PROshould be sought. Additionally, PROs might be required periodically overa long period of time. Some diseases and corresponding treatments maylast months, years, or be persistent over a lifetime. The longitudinalinformation that would come from PROs is valuable in determiningefficacy of treatments and in stratifying diseases diagnosed based onsymptoms, to determine the underlying molecular basis for the disease.

To data solutions to fill in the holes and acquire the missinginformation have all been focused on using humans to review the data,identify mission information, and manually attempt to fill the gaps.People can for instance read EHR records, including fee form fields, andthen populate the digital fields accordingly. Additionally surveyorscall individuals and through in person interview they identify PROs, andinput the corresponding information into digital database fields. In allcases the solution requires the intervention of an individual, and it istherefor not scalable in terms of labor hours or labor dollars for usewith databases of tens of thousands to millions of individuals.

Beyond EHR and PRO data, many other data types may be extremely usefulin piecing together an overall picture of the condition, state, orhealth of an individual and of groups of individuals, as well as forassessing possible pathways for maintaining health, avoiding or treatingdisease, recognizing and developing treatments, and so forth. But suchpathways are hindered by missing or inaccurate data, and by typical“siloing” of data by separate sources, institutions, and so forth, oftenwith no ill intent, and many times with patient or individualconfidentiality in mind. At the same time, social media platforms mayalmost certainly share data, but again typically silo the data for theirown purposes, and quite often even without any control by theindividuals involved, and little or no quality control.

There is a need for improved technologies for data gathering and qualitycontrol, which scalable data aggregation and use. There is a particularneed for such technologies that may enhance the control and motivateparticipation by contributing individuals, while protecting theirprivacy.

BRIEF DESCRIPTION

In accordance with one aspect of the disclosure, a system comprises aserver that, in operation, facilitates interaction with contributingmembers of an aggregation; a centralized database maintained by anadministrative entity that, in operation, stores and aggregates themember-specific contributed data transmitted by contributing memberswith member-specific contributed data contributed by other contributingmembers; and processing circuitry maintained by the administrativeentity that, in operation, processes member-specific account datareceived from the contributing members via interface pages to establishmember-specific accounts based on the member-specific account data, andattributes a member-specific value to the member-specific accounts basedupon respective member-specific contributed data. The processingcircuitry analyzes the member-specific contributed data for each memberto determine missing or incorrect data, and sends a de-identifiedcommunication to respective member to provide missing data or to correctincorrect data.

In accordance with some embodiments, the processing circuitry maydetermine a quality score based upon the completeness and/or correctnessof the member-specific contributed data; and/or the quality score isbased at least partially on determined contradictions and/orinconsistencies in the member-specific contributed data; and/or themember-specific value is at least partially upon the quality score;and/or the processing circuitry automatically and without humanintervention attempts to complete missing data and/or to correctincorrect data prior to communicating with the contributing member;and/or the database is configured to store member-specific contributeddata of different types, and the processing circuitry determines missingor incorrect data of one type based upon analysis of data of a differenttype; and/or the types of member-specific contributed data comprise atleast two of omic data, phenotype data, health data, personal data,familial data and environmental data; and/or the database is configuredto store member-specific contributed data of different types, and theprocessing circuitry determines missing or incorrect data of one typebased upon analysis of the same type of data but contributed atdifferent times by the same contributing member; and/or the missing orincorrect data is determined based upon analysis of aggregated data of aplurality of contributing members; and/or the missing or incorrect datacomprises at least two of personal data, medical record data, dietarydata and wearable device data; and/or the communication comprises acustomized survey based upon data determined to be missing and/orincorrect; and/or the communication comprises a recommendation foracquisition of additional data by the contributing member; and/or theprocessing circuitry transfers an asset amount to each member-specificaccount based upon the member-specific value; and/or the user-specificvalue is attributed as a currency and/or a cryptocurrency and/or anownership share in the database; and/or the processing circuitry isconfigured to make ledger entries in an immutable and/orcryptographically encoded ledger and/or a blockchain based uponinteraction with the contributing members.

In accordance with another aspect of the disclosure, a system comprisesa server that, in operation, facilitates interaction with contributingmembers of an aggregation; a centralized database maintained by anadministrative entity that, in operation, stores and aggregates themember-specific contributed data transmitted by contributing memberswith member-specific contributed data contributed by other contributingmembers; and processing circuitry maintained by the administrativeentity that, in operation, processes member-specific account datareceived from the contributing members via interface pages to establishmember-specific accounts based on the member-specific account data, andattributes a member-specific value to the member-specific accounts basedupon respective member-specific contributed data, wherein the processingcircuitry analyzes the member-specific contributed data for each memberto determine missing or incorrect data; and wherein the database isconfigured to store member-specific contributed data of different types,and the processing circuitry automatically and without humanintervention, provides and/or corrects missing or incorrect data of onetype based upon analysis of data of a different type or from a separatecontribution event, the types of member-specific contributed datacomprise at least two of omic data, phenotype data, health data,personal data, familial data and environmental data.

In accordance with some embodiments of this technique, the databasecomprises an immutable and/or cryptographically encoded and/ortamper-evident ledger; the processing circuitry sends a de-identifiedcommunication to respective contributing members to provide or correctmissing or incorrect data, or to confirm the automatic provision of themissing or incorrect data; or any of the specific variations mentionedabove may be combined with these embodiments.

In accordance with a further aspect of the disclosure, acomputer-implemented method comprises receiving, from the contributingmembers, member-specific account data and member-specific contributeddata, the member-specific contributed data comprising health-relateddata submitted by each contributing member or data derived therefrom;storing, in a database, the member-specific contributed data;aggregating the member-specific contributed data with member-specificcontributed data of other contributing members; establishing amember-specific account for each contributing member based on themember-specific account data; attributing a member-specific value toeach member-specific account based upon member-specific contributed dataof the respective contributing member; and processing themember-specific contributed data to determine missing or incorrect data,and sending a de-identified communication to respective member toprovide missing data or to correct incorrect data.

In accordance with some embodiments of such methods, the database oranother component of the system comprises an immutable and/orcryptographically encoded ledger and/or a blockchain. Any of thespecific variations mentioned above may be combined with such methods.

In accordance with still another aspect of the disclosure, a systemcomprises a server that, in operation, facilitates interaction withcontributing members of an aggregation; a centralized databasemaintained by an administrative entity that, in operation, stores andaggregates the member-specific contributed data transmitted bycontributing members with member-specific contributed data contributedby other contributing members; and processing circuitry maintained bythe administrative entity that, in operation, processes member-specificaccount data received from the contributing members via interface pagesto establish member-specific accounts based on the member-specificaccount data, and attributes a member-specific value to themember-specific accounts based upon respective member-specificcontributed data; wherein the processing circuitry analyzes themember-specific contributed data for each member to determine missing orincorrect data, and sends a de-identified communication to respectivemember to provide missing data or to correct incorrect data; and whereinthe processing circuitry automatically and without human interventionattempts to complete missing data and/or to correct incorrect data priorto communicating with the contributing member.

In accordance with some embodiments of this system, the database isconfigured to store member-specific contributed data of different types,and the processing circuitry determines missing or incorrect data of onetype based upon analysis of data of a different type or data contributedat a different time; and/or the processing circuitry attempts tocomplete missing data and/or to correct incorrect data of one type basedupon analysis of a different type; and/or the processing circuitryattempts to complete missing data and/or to correct incorrect data ofone contributing member based upon member-specific contributed data ofat least one other contributing member; and/or the processing circuitryattempts to complete missing data and/or to correct incorrect data ofone contributing member based upon aggregated member-specificcontributed data of a plurality of other contributing members; and/orthe types of member-specific contributed data comprise at least two ofomic data, phenotype data, health data, personal data, familial data andenvironmental data; and/or the missing or incorrect data comprises atleast two of personal data, medical record data, dietary data andwearable device data; and/or the communication comprises a customizedsurvey based upon data determined to be missing and/or incorrect, or aninvitation to provide additional data; and/or the processing circuitrydetermines a quality score based upon the completeness and/orcorrectness of the member-specific contributed data; and/or the qualityscore is based at least partially on determined contradictions and/orinconsistencies in the member-specific contributed data; and/or theprocessing circuitry re-evaluates the quality score after completion ofincomplete data and/or correction of incorrect data; and/or themember-specific value is at least partially upon the quality score;and/or the processing circuitry re-evaluates the member-specific valueafter completion of incomplete data and/or correction of incorrect data;and/or the processing circuitry transfers an asset amount to eachmember-specific account based upon the member-specific value; and/or theuser-specific value is attributed as a currency and/or a cryptocurrencyand/or an ownership share in the database; and/or the processingcircuitry is configured to make ledger entries in an immutable and/orcryptographically encoded ledger and/or a blockchain based uponinteraction with the contributing members. Methods, includingcomputer-implemented methods may be implemented to utilize suchtechniques, including any of the specific variations mentioned above.

In accordance with still another aspect of the disclosure, a systemcomprises a server that, in operation, facilitates interaction withcontributing members of an aggregation; a centralized or virtuallycentralized database maintained by an administrative entity that, inoperation, stores and aggregates the member-specific contributed datatransmitted by contributing members with member-specific contributeddata contributed by other contributing members; and processing circuitrymaintained by the administrative entity that, in operation, processesmember-specific account data received from the contributing members viainterface pages to establish member-specific accounts based on themember-specific account data, and attributes a member-specific value tothe member-specific accounts based upon respective member-specificcontributed data; wherein the processing circuitry analyzes themember-specific contributed data for each member to determine missing orincorrect data, and sends a de-identified communication to respectivemember to provide missing data or to correct incorrect data; and whereinthe processing circuitry automatically and without human interventionsends follow-up de-identified communications to specific contributingmembers to prompt contribution of follow-up member-specific contributeddata based upon a physical condition of the respective contributingmembers.

In accordance with some embodiments of this aspect, the processingcircuitry sends the follow-up de-identified communications periodically;and/or the processing circuitry sends the follow-up de-identifiedcommunications episodically; and/or the processing circuitry sends thefollow-up de-identified communications based upon treatment regimes asindicated by the member-specific contributed data for the respectivecontributing members; and/or the processing circuitry sends thefollow-up de-identified communications based upon a condition and/ordisease diagnosis as indicated by the member-specific contributed datafor the respective contributing members; and/or the follow-upde-identified communications comprise recommendations for acquisition ofadditional data of the respective contributing members; and/or themember-specific contributed data comprises health-related data, and therecommendations comprise at least one physical examination or testrelated to physical condition of the respective contributing members;and/or the processing circuitry analyzes the member-specific contributeddata of each member to determine at least one most convenient and/orcost effective source for the acquisition of additional data, and therecommendations include an indication of the most convenient and/or costeffective source for each respective recommendation to each respectivecontributing member; and/or the follow-up communications for onecontributing member are based upon member-specific contributed data ofat least one other contributing member; and/or the follow-upcommunications for one contributing member based upon aggregatedmember-specific contributed data of a plurality of other contributingmembers; and/or types of member-specific contributed data comprise atleast two of omic data, phenotype data, health data, personal data,familial data and environmental data; and/or the follow-upcommunications comprise a customized survey based upon the physicalcondition of the respective contributing members; and/or the processingcircuitry re-evaluates the member-specific value after receipt offollow-up member-specific contributed data from each respectivecontributing member; and/or the follow-up communications comprise anindication to each contributing member of the re-evaluation of themember-specific value applicable when the respective contributing membercontributes the follow-up member-specific contributed data; and/or theprocessing circuitry transfers an asset amount to each member-specificaccount based upon the member-specific value; and/or the user-specificvalue is attributed as a currency and/or a cryptocurrency and/or anownership share in the database; and/or the processing circuitry isconfigured to make ledger entries in an immutable and/orcryptographically encoded ledger and/or a blockchain based uponinteraction with the contributing members. Methods, includingcomputer-implemented methods may be implemented to utilize suchtechniques, including any of the specific variations mentioned above.

In accordance with still another aspect of the disclosure, a systemcomprises a server that, in operation, facilitates interaction withcontributing members of an aggregation; a centralized or virtuallycentralized database maintained by an administrative entity that, inoperation, stores and aggregates the member-specific contributed datatransmitted by contributing members with member-specific contributeddata contributed by other contributing members; and processing circuitrymaintained by the administrative entity that, in operation, processesmember-specific account data received from the contributing members viainterface pages to establish member-specific accounts based on themember-specific account data, and attributes a member-specific value tothe member-specific accounts based upon respective member-specificcontributed data; wherein the processing circuitry analyzes theaggregated member-specific contributed data to determine cohorts ofcontributing members based upon correlations between the member-specificcontributed data for each contributing member.

In accordance with some embodiments of such systems, the processingcircuitry determines the cohorts by periodic analysis of the aggregatedmember-specific contributed data; and/or the processing circuitrydetermines the cohorts by episodic analysis of the aggregatedmember-specific contributed data; and/or the processing circuitrydetermines the cohorts based upon analysis of the aggregatedmember-specific contributed data initiated by at least one contributingmember; and/or the processing circuitry determines the cohorts basedupon analysis of the aggregated member-specific contributed datainitiated by identification of a physical condition potentiallydetectable from the aggregated member-specific contributed data; and/orthe processing circuitry determines the cohorts based upon analysis ofthe aggregated member-specific contributed data initiated byidentification of a new treatment of a physical condition detectablefrom the aggregated member-specific contributed data; and/or theprocessing circuitry determines the cohorts based upon analysis of theaggregated member-specific contributed data initiated by identificationof a new examination, test, or omic pattern useful in determining aphysical condition detectable from the aggregated member-specificcontributed data; and/or the processing circuitry determines the cohortswithout identification of the contributing members to the administrativeentity; and/or the cohorts comprise contributing members sharing aphysical condition; and/or the cohorts comprise contributing memberssharing a disease state; and/or the cohorts comprise contributingmembers sharing a potential legal claim; and/or the processing circuitrypermits communications between contributing members of a cohort withoutrevealing identification of respective contributing members to theadministrative entity; and/or the processing circuitry permitscommunications between contributing members of a cohort withoutrevealing identification of respective contributing members to othercontributing members unless such identification is done by therespective contributing members; and/or types of member-specificcontributed data comprise at least two of omic data, phenotype data,health data, personal data, familial data, demographic data, employmentdata, and environmental data; and/or the determination of cohorts isinitiated based upon analysis of one type of data followed by analysisof different types of data; and/or the processing circuitry permitscontributing members to opt out of analysis to determine cohorts; and/orthe processing circuitry permits contributing members to request thatother members contribute additional data to enable or improve astatistical fit of data from potential cohort members of a determinedcohort group; and/or the processing circuitry is configured to performquality control operations on the contributed data prior todetermination of the cohorts; and/or the processing circuitry transfersan asset amount to each member-specific account based upon themember-specific value; and/or the user-specific value is attributed as acurrency and/or a cryptocurrency and/or an ownership share in thedatabase; and/or the processing circuitry is configured to make ledgerentries in an immutable and/or cryptographically encoded ledger and/or ablockchain based upon interaction with the contributing members.Methods, including computer-implemented methods may be implemented toutilize such techniques, including any of the specific variationsmentioned above.

In accordance with yet another aspect of the disclosure, a systemcomprises a server that, in operation, facilitates interaction withcontributing members of an aggregation; a centralized or virtuallycentralized database maintained by an administrative entity that, inoperation, stores and aggregates the member-specific contributed datatransmitted by contributing members with member-specific contributeddata contributed by other contributing members; and processing circuitrymaintained by the administrative entity that, in operation, processesmember-specific account data received from the contributing members viainterface pages to establish member-specific accounts based on themember-specific account data, and attributes a member-specific value tothe member-specific accounts based upon respective member-specificcontributed data; and a template stored in database and includinganticipated events or information in a patient health journey; whereinthe processing circuitry automatically and without human interventionsends follow-up de-identified communications to specific contributingmembers to prompt contribution of follow-up member-specific contributeddata based upon the template.

In accordance with some embodiments of such systems, the systemcomprises a plurality of templates, each template including anticipatedevents in a different patient health journey; and/or the template isbased upon analysis of the aggregated member-specific contributed dataindicative of events of other contributing members on the same patienthealth journey; and/or the template is based on contributed data frommembers who may have a similar condition or symptoms; and/or thefollow-up communications based upon the template relate to a patienthealth journey initiated by a birth; and/or the follow-up communicationsbased upon the template relate to a patient health journey initiated bysymptoms or conditions indicated by the patient-specific contributeddata; and/or the follow-up communications based upon the template relateto a patient health journey initiated by a diagnosis indicated by thepatient-specific contributed data; and/or the follow-up communicationsbased upon the template relate to a patient health journey initiated bya treatment plan indicated by the patient-specific contributed data;and/or the follow-up communications based upon the template relate to apatient health journey initiated by identification of a physicalcondition potentially detectable from the aggregated member-specificcontributed data; and/or the follow-up communications based upon thetemplate relate to a patient health journey initiated by identificationof a new treatment of a physical condition detectable from theaggregated member-specific contributed data; and/or the follow-upcommunications based upon the template relate to a patient healthjourney initiated by identification of a new examination or test usefulin determining a physical condition detectable from the aggregatedmember-specific contributed data; and/or the follow-up communicationsbased upon the template comprise a custom report adapted to facilitate acontributing member consulting a medical professional; and/or thefollow-up de-identified communications comprise recommendations foracquisition of additional data of the respective contributing members;and/or the member-specific contributed data comprises health-relateddata, and the recommendations comprise at least one physical examinationor test related to physical condition of the respective contributingmembers; and/or the processing circuitry analyzes the member-specificcontributed data of each member to determine at least one mostconvenient and/or cost effective source for the acquisition ofadditional data, and the recommendations include an indication of themost convenient and/or cost effective source for each respectiverecommendation to each respective contributing member; and/or theprocessing circuitry re-evaluates the member-specific value afterreceipt of follow-up member-specific contributed data from eachrespective contributing member; and/or the follow-up communicationscomprise an indication to each contributing member of the re-evaluationof the member-specific value applicable when the respective contributingmember contributes the follow-up member-specific contributed data;and/or the processing circuitry transfers an asset amount to eachmember-specific account based upon the member-specific value; and/or theuser-specific value is attributed as a currency and/or a cryptocurrencyand/or an ownership share in the database; and/or the processingcircuitry is configured to make ledger entries in an immutable and/orcryptographically encoded ledger and/or a blockchain based uponinteraction with the contributing members. Methods, includingcomputer-implemented methods may be implemented to utilize suchtechniques, including any of the specific variations mentioned above.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a diagrammatical representation of an example platform for thecontribution, analysis, aggregation, and management of member data;

FIG. 2 is a diagrammatical representation of an example data and filecontribution process;

FIG. 3 illustrates various example data types that may be contributedand handled by the system and process;

FIG. 4 illustrates analytical ways in which the data may relate to acontributing member;

FIG. 5 illustrates different data types and ways in which gaps in datamay be identified and filled;

FIG. 6A illustrates certain data originating technologies for sensing ormonitoring bio-related data for a member;

FIG. 6B illustrates an example data originating workflow, such as formedical-related data;

FIG. 6C illustrates another example data originating workflow, such asfor omic data;

FIG. 7 is a diagrammatical representation of an example interplaybetween certain data originating and acquisition approaches, and aprocessing and analysis system that may be maintained and operated by anadministrative entity;

FIG. 8 represents an example manner in which such data originating andacquisition approaches may come into play in a “patient journey”addressing a member physical condition;

FIG. 9 is a flow chart illustrating an example account initiation andmember interaction process;

FIG. 10 is a diagrammatical representation of an example process fortransparent confidentiality in processing member data;

FIG. 11 is a diagrammatical representation of an example process formember data processing and quality control;

FIG. 12 is a flow chart illustrating an example data gap or erroridentification and correction process;

FIG. 13 is a flow chart illustrating an example process for recommendingdata gap filling, complementing, and completion;

FIG. 14 is a flow chart illustrating an example process for identifyingpotentially useful new data for members, and recommending follow-up; and

FIG. 15 is a flow chart illustrating an example process for analyzingmember data to provide enhanced value by determining cohorts amongmembers for possible data sharing and follow-up.

DETAILED DESCRIPTION

SYSTEM AND METHOD OVERVIEW: The present disclosure relates totechnologies disclosed in international patent application numberPCT/US2018/061419, filed on Nov. 16, 2018 by LunaPBC, and entitled“PERSONAL, OMIC, AND PHENOTYPE DATA COMMUNITY AGGREGATION PLATFORM”,which is hereby incorporated into the present disclosure by reference inits entirety and for any and all purposes.

The inventions disclosed here aim to build systems, platforms, processesand technologies for collecting contributed data from members of acommunity, compensating such contributions, and providing enhancedquality of the data along with novel uses. The unique and novelmethodologies are scalable, and maintain privacy, while moving towardsmore complete and accurate data of many types and sources.

In accordance with some aspects of the disclosure, solution to the datacollection, compensation, aggregation, completion, and accuracy problemsmay involve several steps or phases. First, computer assisted techniquesare used, which may employ machine learning or similar algorithmicmethods to identify missing data components. For instance, it ispossible to identify when individuals visit a health care provider for aspecific condition and to identify a diagnosis of a professional and acorresponding treatment recommendation. Using this information thesoftware can then search out whether information exists on the patientsadherence to the treatment along with treatment results short term andlong term. Automatic identification of this missing information usingalgorithms, computers, or other logic-based approaches may be the firststep in the process.

The system can also identify when a healthcare provider did not use adigital entry field and instead used a freeform comment field. Oneinitial step could be to simply digitally analyze the comments andattempt to translate comments, possibly using natural languageprocessing, into digital database field entries. In some cases thesystem would not be able to parse comments or interpret manual entries.In these later cases a person may be needed to read the manual entry andprovide interpretation. In some of these cases, automatedcommunications, questions, customized surveys and the like could begenerated and sent to the contributing member of the community tocomplete, provide, or correct any submitted data.

Another source of missing information could be during the formation ofdisease cohorts. It would be possible to identify factors common amongthe case cohort and to rate or analyze the information with respect to acandidate or control cohort. In these cases the statistical power of thestudy would be increased if missing information from a subset of cohortmembers was able to be addressed and added to the study.

Also, the system could automatically compare member conditions,symptoms, or reported diseases with those of other community members,assemble members into virtual cohorts around diseases or conditions, andthen identify possibly important information contained in other memberrecords but missing from the records of other members in the virtual orcandidate cohort. The missing information could be an analytical test,imaging modality, or questions regarding health environment or qualityof life.

Solutions to fill in information gaps may include techniques such asutilizing machine learning, data imputation from a large datasets,and/or the automated creation and distribution of custom user surveys.For example, when PROs are identified as missing from the record, thesystem would review all the existing data records of an individualassociated with the condition in question, including reported symptomsand prescribed treatment. Based on this information, a custom surveycould be created to retrieve information, such as whether the individualfollowed the treatment and whether the symptoms were resolved or newsymptoms are emerging. This survey could be time-based if the conditionis current, or retrospective if the condition is based on past medicalhistory.

Another solution for addressing gaps in PRO information would be toquery the database, such as to assess whether the records show thepurchase of prescriptions prescribed by the healthcare professional,whether future health records reveal information on the listed symptom,or whether information on the condition could be imputed from otherrecords in the database. Following these examples, for instance, whengaps in EHRs are identified due to missing fields, or conflictinginformation exists in the records a custom survey could be generatedspecifically to fill the gaps, and the survey could be sent out to theindividual.

Many uses may be made by analyzing member-specific contributed data, aswell as aggregated data from many members. For example, the systemitself or members of the community may initiate or carry outself-assembly of de-identified community members into cohorts arounddiseases or other important issues (e.g., health, treatment, lifestyle,legal, etc.). In some use cases, members of the community may want tosearch out other members with similar chronic or acute diseases. A valuein the short term may be for members to share information with eachother to solicit suggestions on what may or may not help in alleviatingthe condition in question. A long term value may be the internallydriven creation of narrow disease (or “condition” or “situation”)cohorts that could be leveraged for future discoveries of links, such asbetween disease and the genome, microbiome or other social determinantsof health. This discovery would be made possible by the initial selfassembly of the cohort, the identification and retrieval of potentiallyrelevant missing information, and the use of tools to identifycorrelations that exist in the case group but may be rare in a controlgroup.

One important challenge is the enabling of members to come together andcommunicate while also protecting their anonymity. It will be possibleto use the disclosed platform to enable members to search each other outbased on phenotypic or genomic signatures, or other data types (e.g.,disease or condition diagnoses, symptoms, demographics, geographicinformation, ethnicity, employers, life history, etc.). Members couldthen anonymously send out invitations or e-mails to other membersrequesting they connect in an internal chat room or join an internalgroup, for example. Once in a group, members could communicateinformation related to a specific disease or condition and determinewhether others in the group share the condition. They could also thenidentify missing information and trigger a request to fill out a surveyor to pursue an analytical test by an external professional, forexample. Results could be uploaded and compared to further refine acohort. Continuous queries could be generated to identify unique genomicor phenotypic correlations associated with the disease or condition inquestion. A reliable platform to seek out other members based on diseasecondition, communicate with other members, request retrieval ofimportant missing information, and search for discovery links isinvaluable and not possible today.

One underlying premise behind the present techniques is that a communityownership plan creates a people-driven scientific enterprise perceivedas one worth joining, and based upon the premise that the best way toencourage new contributors to join and broadly consent to the use oftheir data is to make them full and engaged partners in the project.This may imply databases that will be 100% owned, or partially owned bydata contributors, who will gain increased stakes as they contributegenomic data, phenotype data, health, and other personal data ofinterest. Unlike other approaches in the field, proceeds generated byproviding access to the data (for instance to pharmaceutical companies)will be apportioned among the community based on for instance total andtypes of contributions to the community. Furthermore in order to providecontributors control over their data, contributors will have the abilityto withdraw consent by returning their original stake or a stake ofcommensurate value at any point.

Aside from a formal stake in the enterprise (sometimes referred to inthe present disclosure as “the system”), partnership may also meanseeing the studies and the results of such studies that are performed,and having the opportunity to provide feedback on what is happening withthe database. For many participants, a primary motivation will be tosupport the greater good through scientific discovery. The system or thesystem administrator or sponsor may aim to encourage this type ofparticipation through regular communications to build trust in themanagement of the database, and its contributions to science.

Community ownership solves many of the problems of trust and datacontrol that act as obstacles to participation in biomedical studies,but for it to be effective the mechanism of ownership cannot itselfbecome an obstacle. Encrypted databases, cryptographic ledgers, andrepresentations of ownership stake, such as cryptocurrency coins andsimilar devices may provide a straightforward and hassle-free means toimplement decentralized and large scale ownership. After making theirinitial data contribution, participants may earn additionalparticipation, ownership, coins, etc. (sometimes collectively referredto in the present disclosure as “value”) for contributing additionaldata.

A useful goal may be to identify the molecular basis of disease, causesof a chronic disease, or social determinants of disease, even if theeconomics are insufficient for commercial organizations to underwritethe efforts. In such cases the system could partner with nonprofitorganizations.

One long term goal may be increasing the value of the “coins” or “value”attributed to data contributors (sometimes referred to in the presentdisclosure as “members”) by maximizing the value of the database. Thisgoal will incentivize members or collaborators to partner with allplayers in the ecosystem even at the expense of short term profits(e.g., partner of choice in the full ecosystem). It will also aligngoals with those of the member community (i.e., focus on the intrinsicbenefits and intangible satisfaction of solving life's most importantproblems).

In some embodiments, new data contributors may receive a digital “walletID” and a custom cryptocurrency coin, that is designed to represent ortrack the value of the database asset, every time they contributeadditional data. A wide range of genomic and omic data types may beaccepted, including for example SNP array data, DNA sequencing data,somatic genome data, methylome data, virome data, pathegenomic data, andmicrobiome data. High-quality health, medical, and environmental datamay also be accepted, including electronic medical records, surveys ondiet and exercise, health history, and data from wearable devices, andpersonal and/or demographic data that might prove insightful forresearch. (It should be noted that in some embodiments, and for someuses, even lower quality or weaker data may be very helpful, such as forlarge scale studies and where other data is available to complete orcomplement such data.) Environmental data may also be accepted such aswater quality, weather, air quality, and other data relating to anindividual's exposome. The exposome can be defined as the measure of allthe exposures of an individual in a lifetime and how those exposuresrelate to health. The database(s) may also accept data pertaining tonon-human subjects and organisms, including animals, plants, microbes,viruses, fungi, or even “environmental” data such as to determine allpossible organisms present.

In certain disclosed embodiments, the system calls upon a server that,in operation, serves interface pages to contributing members of anaggregation community for receipt of member-specific account data andmember-specific contributed data. The member-specific contributed datamay comprise any of a vast range of data types. A database is maintainedby an administrative entity that, in operation, stores and aggregatesthe member-specific contributed data with member-specific contributeddata contributed by other contributing members. Processing circuitrymaintained by the administrative entity processes member-specificaccount data received from the contributing members via the interfacepages to establish member-specific accounts based on the member-specificaccount data, and attributes a member-specific value to themember-specific accounts based upon respective member-specificcontributed data.

The contributed data is subjected to one or many forms of analysis todetermine its completeness and accuracy (sometimes more broadly referredto as its “quality”). Where possible, the data entries or fields may beautomatically completed or corrected. Additional data may be solicitedfrom the members, and communications may be made on the basis ofidentified quality issues. But many other communication initiators maybe contemplated, such as based upon anticipated “pathways” or “journeys”of the members, such as through life, through treatments, throughdisease discovery, diagnosis, and management, and so forth. The analysismay, in fact, be continuous, periodic, or episodic. It may be based, forexample, upon recognition of a possible condition or life-improvingactivity that becomes apparent from the contributed (and aggregated)data. Recommendations for acquiring or providing additional data may bemade that can improve the accuracy or certainty of determinations of theconditions (e.g., a diagnosis, preventative measures, etc.). A number ofthe possible processing and use cases are discussed in this disclosure.

In certain of embodiments, the processing circuitry attributes themember-specific value based upon a pre-established calculation appliedto all contributing members. An asset value or amount may be transferredor applied to each member-specific account as consideration formember-specific contributed data of the respective contributing member.By way of example, an asset amount may be calculated by a formula havinga generalized form:

F=x/y;

wherein F is the fraction of ownership; x is the sum of ((W1)×(sum ofdata units of a first type of data unit)+(W2)×(sum of data units of asecond type of data unit)+(W3)×(sum of data units of a third type ofdata unit) . . . +(Wn)×(sum of data units of an n type of data unit))associated with the account; y is the sum of ((W1)×(sum of data units ofthe first type of data unit)+(W2)×(sum of data units of the second typeof data unit)+(W3)×(sum of data units of the third type of data unit) .. . +(Wn)×(sum of data units of the n type of data unit)) associatedwith all accounts; and W1, W2, W3 . . . Wn are optional weightingfactors, and/or the database is configured to store member-specificcontributed data of different types. In such embodiments, the processingcircuitry may attribute the member-specific value based upon types ofmember-specific contributed data submitted by each member, and thequality of the data. As mentioned, many different data types may beinvolved, such as (and many of these are super- or sub-sets of oneanother, or overlap one another): omic and phenotype data, health data,personal data, familial data, environmental data genomic data,microbiomic data, epigenomic data, transcriptomic data, proteomic data,genotype data, single nucleotide polymorphism data, short tandem repeatdata, microsatellite data, haplotype data, epigenomic data, genomemethylation data, microbiomic data, whole or partial gene sequence data,whole or partial exome sequence data, whole or partial chromosome data,whole or partial genome sequence data, medical record data, exercisedata, dietary data, and wearable device data. In some embodiments, thedatabase is configured to separately store member-specific contributeddata for a respective member personally, an animal, plant, or microbialspecies owned or controlled by a respective member, and an environmentowned or controlled by a respective member.

In some embodiments, limited access to member-specific contributed dataand/or aggregated data may be offered to third-parties, such as on thebasis of contractual arrangements with the administrative entity of thesystem, and any remuneration from such activities may flow back to thecontributing members. It is contemplated that only de-identifiedcontributed data will be accessed based on third-party submitted studydesign criteria. These criteria may be used to query the system'sdatabase(s) for appropriate information to include in a possible study.The relevant data is only identified based on a unique identifierindependent from member personal information (that is, in ade-identified manner that does not permit personal identification of themembers). Once subsets of information are identified, the informationmay be aggregated and populated in a secure, private logic-controlled“sandbox” within the system's secure cloud service site for analysis bythird-parties who may be interested in analysis, tests, studies, andresearch based on the aggregated member data. In some situations (i.e.,clinical trial recruitment), the third-party may be interested incontacting members directly. The system may enable this via an anonymousprocess that leverages the unique identifier associated with themembers' data, which allows the third-party to invite members into adirect communication (but in present embodiments the third-party stillhas no knowledge of the members' personal information). It is then themembers' choice whether they will engage in direct contact with thethird-party or not. Preferences to receive these invitations can beturned on or off within a profile page of each member's account. Allinformation in the system only includes what members voluntarilyauthorize to share.

Moreover, at any time, members can choose to delete some or all of theirshared information from the system, and withdrawal of information willimpact the member's ownership or value stake in the system. In allevents, the member is the owner of their data.

In some embodiments, the system may provide training or educationalmaterials to members, which may assist in the acquisition andcontribution of higher-quality data, the completion of contributed data,and the correction of such data. Educational materials may include, forexample, videos, textual presentations, exercises, and so forth, and maybe structured as modules that members may progressively access to betterunderstand both the workings of the system, their options in the system,and more broadly, any aspect of the data contributed, its use, itsbenefits, any recommendations made, any cohorts determined, and soforth. In some cases, the shares, value attributed, or some other formof compensation may be linked to the completion of stages in sucheducational materials.

TERMS AND CONCEPTS: Through the present disclosure, certain terms andconcepts are referred to in embodiments of the technology described.These may be understood by their ordinary and customary meaning in theart, and in view of any special meaning used in the present context, aswill be understood by those skilled in the art. Some of the terms andconcepts include:

Data

-   -   member-specific account data: information relating to a members        residence, contact info, tax filing number, ownership stake,        birth date, etc.;    -   member-specific contributed data: personal, health, medical,        environment, historic, and omic data that is specific to a        person contributing the information;    -   data: depending upon the context, the general term data may        apply to account data, contributed data, data based upon one or        both of these, or to processed and/or aggregated data;    -   data derived from contributed data: metadata, summarized data,        or data emanating from a logical or mathematical analysis of the        member data;    -   medical data: electronic medical and health records, results of        tests either analytical or subjective, medical diaries,        prescriptions etc.;    -   health data: data relating to the health, wellbeing, and quality        of life including sensor data, biometric data, diet tracking,        survey answers related to health, quality of life, family        status, emotional state or condition, health diaries;    -   personal data: data relating to an individual's behaviors,        habits, and daily activities such as geographic locations        visited, purchasing or spending activities, web browsing,        friends, social media posts, employee record, academic records,        etc. (in general, this may include any or all data relating to        an individual, including genomic, health medical, etc.);    -   familial data: family history including health and medical        history, lineage, and genealogy;    -   environmental data: envirome and exposome data encompasses a)        all of the environmental conditions required for successful        biological life that affect human health, and b) life-course        environmental exposures (including lifestyle factors), from the        prenatal period onwards, including quality and chemical, omic,        or organic content of air, water, climate, and soil;    -   genomic data—relating to the make-up of an individual germ-line        DNA and data related to somatic mutations including cancer DNA        information, typically all cells in an individual's body contain        the same genomic data with only minor variations, but not        always;    -   microbiomic data: relating to the nucleotide sequence or        taxonomic classification of other organisms that exist        symbiotically, parasytically, or commensal with an individual;        common locations of these communities are hand, sinuses, mouth,        gut, rectum, sex organs, etc.; also included is pathegonomic and        viromid data, covering deleterious microbes, fungi, and viruses;    -   epigenomic data: relating to genomic data that impacts the        expression of a person's genome from DNA sequence data to        proteins, including for example DNA methylation, histone        wrapping, etc.; epigenomic data can be different cell to cell in        the body and tissue type to tissue type;    -   transcripomic data: the set of all RNA molecules in one cell or        a population of cells, often with expression level values        included;    -   proteomic data: a list of proteins occurring within a cell or        group of cells, often with relative abundance values;    -   pathogenomic data: genomic data and/or phenomic data on        pathogens that affect human health; however, studies also exist        for plant and animal infecting microbes. These pathogens may        include bacteria, viruses, and fungi.    -   genotype data: relating to determining single nucleotide        polymorphisms “SNPs” or single basepair difference between        individuals (e.g., A, C, T, G), data sets often including        insertions of a single base and deletions of a single base when        discussing consumer genomic genotyping data results;    -   single nucleotide polymorphism data: a variation in a single        nucleotide that occurs at a specific position in the genome,        often called “SNPs”;    -   short tandem repeat data: a short tandem repeat is a        microsatellite, consisting of a unit of two to thirteen        nucleotides repeated hundreds of times in a row on the DNA        strand;    -   microsatellite data: a microsatellite is a tract of repetitive        DNA in which certain DNA motifs (ranging in length from 1-6 or        more base pairs) are repeated, typically 5-50 times;    -   structural variants: a region of DNA approximately 1 kb and        larger in size and can include inversions and balanced        translocations or genomic imbalances (insertions and deletions),        commonly referred to as copy number variants (CNVs);    -   haplotype data: a set of DNA variations, or polymorphisms, that        tend to be inherited together. A haplotype can refer to a        combination of alleles or to a set of single nucleotide        polymorphisms (SNPs) found on the same chromosome;    -   genome methylation data: a list of bases or sets of bases that        have been methylated, a process where methyl groups are added to        DNA base;    -   whole or partial gene sequence data: a succession of letters        that indicate the order of nucleotides forming alleles within a        gene;    -   whole or partial exome sequence data: a succession of letter        that indicate the part of the genome composed of exons, the        sequences which, when transcribed, remain within the mature RNA        after introns are removed by RNA splicing and contribute to the        final protein product encoded by that gene;    -   whole or partial chromosome data: a succession of letter that        indicate the sequence of whole or part of a chromosome;    -   whole or partial genome sequence data: a succession of letters        that indicate the order of nucleotides forming alleles within a        DNA (using GACT) or RNA (GACU) molecule;    -   medical record data: a patient's individual medical record data        identifies the patient and contains information regarding the        patient's case history at a particular provider; the health        record as well as any electronically stored variant of the        traditional paper files contain proper identification of the        patient;    -   exercise data: covering any activity, or lack thereof, requiring        physical effort, carried out especially to sustain or improve        health and fitness;    -   dietary data: pertaining to nutritional status, calories        consumed in order to cross-sectionally describe dietary patterns        of consumption and food preparation practices, and to identify        areas for improvement;    -   wearable device data: devices that can be worn by a consumer and        often include tracking information related to health and        fitness; other wearable tech gadgets include devices that have        small motion sensors to take photos and sync with your mobile        devices;    -   biometric device data: include any device that tracks biometric        data, from heart rate monitors to state-of-the-art ingestible        and/or insertable sensors that can provide your granular data        about the interworking of your internal systems;    -   data indicative of at least a portion of the respective        member-specific contributed data: some or all of the contributed        data may be processed and derived data may be kept, stored,        analyzed, etc.; indicative data may include various processed or        encoded forms (e.g., tags, structured data, etc.);    -   structured data or files derived from the received and stored        member-specific contributed data: depending upon the processing        and analysis, structured data, including tagged data, metadata,        etc., may be created based upon raw or partially processed data        contributed by members;    -   low-pass sequencing: a succession of letters that indicate the        order of nucleotides forming alleles within a DNA (using GACT),        typically gathered at a sequence redundancy that is not        sufficient to assemble an individual's full genome, region of        the genome, exome, gene, or chromosome, but is sufficient to        identify genotypes or minor structural variants within the        genome, gene, chromosome, or exome;    -   personally identifiable information: information that can        identify the member, either alone or in combination with other        information;    -   Information gap, incomplete data, missing data: data that is        either not present at all, or that can be improved and/or can be        refined to enable or enhance statistical correlations (e.g.,        obtain an improved P value) and/or confidence levels for such        correlations between data of the same member, data between        members, aggregated data, between member data and aggregated        data, or between any of these and reference data, to make or        support findings, recommendations, decisions, and conclusions;        in some cases such data may be insufficient in breadth, depth,        and/or scale;    -   incorrect or erroneous data: data that is present, but factually        wrong or that appears to be inconsistent with other data (of the        same or a different type);

Actors

-   -   member: any person who contributes data that is aggregated and        who receives a value for the contributed data;    -   administrative entity: a company or entity apart from the        members and from third-party users of the aggregated data, which        interfaces with members to receive data used to create member        accounts, and receives, processes, and aggregates the        contributed data, and then makes the aggregated data available        to third-parties, such as for research and analysis;    -   third-party: a person or entity apart from the members and from        the administrative entity that has an interest in the aggregated        data and that interacts with the administrative entity to        perform operations on the aggregated data, such as searches and        analysis, and who provides remuneration to the member community        in cooperation with the administrative entity (third-parties may        include, for example, pharmaceutical companies, research        institutions, universities, medical institutions, governmental        and quasi-governmental institutions, independent researchers,        and so forth);    -   successor in interest to the respective member: a person or        entity who obtains legal rights to the data of a member (e.g.,        through an estate);    -   data users: institution, researchers, foundations, or        individuals who search or query the aggregated data;    -   cohort: a grouping of contributing members based upon one or        more factors detectable in contributed and aggregated omic and        phenotypic data, such as a physical condition, a diagnosis,        geographic location, a family situation, a predisposition, a        patient journey, and so forth; in determining cohorts several        other factors, data types and data combinations may be        considered (e.g., demographic data, personal data, geographic        data, omic data, employment data, health history, lifestyle,        habits, interests, etc.);

System Components/Subsystems

-   -   database: one or more databases, typically maintained by the        administrative entity, and containing member data, metadata,        data derived from member data, structured data, etc. (databases        may be constructed in conventional manners or by specific        technologies, such as blockchain);    -   processing circuitry: one or more digital processors typically        embodied in one or more computers, servers, dedicated processing        facilities, etc.;    -   cryptographically encoded ledger: a ledger that is encoded to        permit access by cryptographic methods (e.g., based on private        and/or public keys);    -   immutable ledger: a ledger that cannot be changed, or that        cannot be changed without the change being evident;    -   blockchain: a growing list of records, called blocks, which are        linked using cryptography; each block contains a cryptographic        hash of the previous block, a timestamp, and transaction data;        by design, a blockchain is resistant to modification of the        data;    -   centralized database: a searchable data store including a        centrally located database, virtual centralized database, cloud        based database, collection of dis-aggregated databases that are        purpose built for the respective data types, or other        infrastructure having the ability to aggregate data; for the        present purposes, such “centralized” databases may also include        “federated” databases, including databases physically located        and maintained in different locations (e.g., some countries or        jurisdictions may require local or otherwise controlled storage        for their populations, smart phones and other networkable        devices may individually contain databases that are or may be        federated);    -   account database: a database, typically maintained by the        administrative entity that stores member account data, which may        include member-identifying data and data related to ownership of        databases and/or value attributed to a member;    -   contributed data database: a databased that contains        de-identified and/or encrypted data contributed by members; the        data of the contributed data database may be any type of data        mentioned above, for example;    -   account blockchain or distributed ledger protocol: consensus        protocol; a process, encoded in software, by which computers in        a network, called nodes, reach an agreement about a set of data;    -   contributed data blockchain or distributed ledger protocol: a        protocol that utilizes blockchain and/or distributed ledger        technologies for receiving, processing, aggregating and storing        contributed data;    -   universal resource identifier protocol: a Uniform Resource        Identifier is a string of characters that unambiguously        identifies a particular resource; schemes specifying a concrete        syntax and associated protocols define each URI;    -   data key: a digit or physical key which holds a variable value        which can be applied to a string or a text block, in order for        it to be encrypted or decrypted;    -   data key for each member-specific account is stored in an        encrypted manner;    -   one-way pointer: a programming language object that stores the        memory address of another value located in computer memory;    -   secure alternative authentication protocol that maintains a        de-identified nature of the stored member-specific contributed        data;    -   secure alternative authentication protocol comprises accessing a        contact address for the respective contributing member;    -   secure sandbox memory: a virtual space in which software can be        run securely and logic can be applied to control queries and        query responses;    -   secure cloud service site: a platform of servers, whereas your        virtual sites live on multiple computers, eliminating any single        point of failure; such sites are secure, and ultra-reliable, and        generally always online;    -   educational interface pages: interface pages and materials that        may be served to members for educating the members of the        workings of the community system, the details and types of data        that may be contributed, the details and types of value that may        be obtained by joining and participating in the community, as        well as to better educate members regarding such things are how        to improve data quality, how to maintain accurate and up-to-date        data, etc.;    -   segregated data key: data is separated such that accessing one        portion of a record does not automatically allow access to other        portions of the record;    -   segregation data key database: a structured set of data that        contains key (a variable values that is applied to a string or        block of text to encrypt or decrypt it) that is used to encrypt        or decrypt data;    -   patient journey or patient health journey: a series of stages or        events relating to the life, health, or activities of a member        (“patient”); such patient journeys may relate to a disease or        treatable condition, while in other cases it may imply some        portion or aspect of the member's life (e.g., normal care stages        following birth, vaccinations, eye care, regular checkups,        regular monitoring, diet management, etc.);    -   template: an outline of pathway that includes anticipated events        likely to occur in a patient health journey; templates may be        based upon analysis of the aggregated member-specific        contributed data indicative of events of other contributing        members on the same patient health journey, such as contributed        data from members who may have a similar condition or symptoms;    -   artificial intelligence: programming or code stored and        executable by processing circuitry to interpret data, to learn        from such data, and to use those learnings to achieve specific        goals and tasks through flexible adaptation;    -   machine learning: a process based on programming or code stored        and executable by processing circuitry based on algorithms that        may build a mathematical model of sample data, known as        “training data”, in order to make predictions or decisions        without being explicitly programmed to perform the task (it may        be noted that sometimes terms such as artificial intelligence,        machine learning, and deep learning are used interchangeably);    -   survey: a communication, typically sent or made available to        contributing members online, that includes or solicits inputs        (e.g., fields) providing specific data from the members;

Value-Related Components

-   -   member-specific accounts: accounts established for individual        members that allow for contribution of data, management of        member activities, accounting and tracking ownership and/or        value attributed to a member, opting in and out of activities,        etc.;    -   member-specific value: value attributed to individual members by        virtue of their participation in the community, such as by        contribution of data; value may be in one or more forms,        including, for example, ownership shares, currency,        cryptocurrency, tokens, etc.;    -   pre-established calculation: mathematical calculation or logic        based calculation established and officially implemented prior        to usage;    -   asset amount: an amount of something of value, typically        referring to value attributed to members for their participation        in the community;    -   currency: a basis of value, such as money or some other commonly        recognized basis of transaction;    -   cryptocurrency: a digital currency in which encryption        techniques are used to regulate the generation of units of        currency and verify the transfer of funds;    -   member-specific value is at least partially based upon the        quality evaluation: value may be altered (increased or        decreased) based upon a quality, reliability, or similar        determination (e.g., of the data, of a source of the data, of        the contributor, of past interactions, etc.);    -   smart code: executable code that provides for defined steps or        operations recorded in a verifiable manner (e.g., an immutable        ledger);    -   a smart contract: a computer protocol intended to digitally        facilitate, verify, or enforce the negotiation or performance of        a contract (e.g., through the use of smart code);    -   educational module/video: educational materials that may be        provided (e.g., served) to members in a desired sequence to        systematically lead the members through an instructional        program;    -   third-party interface: pages or other materials that may be        served to third-parties to allow for activities such as the        establishment of accounts, requests for studies and searches of        aggregated data, conveyance of value (e.g., remuneration) for        such activities, and potentially for contacting members for        follow-up activities (e.g., clinical studies);

Operations

-   -   aggregate data: data combined from several measurements and/or        inputs; when data are aggregated, groups of observations are        replaced with summary statistics based on those observations;    -   attribute value: to cause value to be created and recognized;    -   transfer remuneration/currency/value: attributed or recorded        compensation of a defined sort, such as in a member account;    -   separately store (data of different types): store and/or        segregate data in different databases;    -   de-identifying member data (e.g., contributed data): data that        has undergone a process that is used to prevent a person's        identity from being connected with information; defined broadly        to include any method or approach to protecting identity during        storage and processing of data, including the use of homomorphic        encryption on encrypted identifiable data, utilized to        facilitate anonymous or blinded data queries, communications,        etc.;    -   the administrative entity does not link member-specific        contributed data to an associated member-specific account in a        manner that would personally identify the respective        contributing member;    -   sending data to the contact address without accessing the stored        member-specific contributed data;    -   quality evaluation: a process used to determine the accuracy,        veracity, and potential value;    -   quality scoring: applying a function or a look-up table in order        to represent the quality of data;    -   determining inconsistency with member-specific contributed data;    -   sending a notice to a contributing member of results of the        quality evaluation;    -   generating a report of results of the quality evaluation;    -   contributor evaluation: analysis of data and/or activities of        members contributing data to determine aspects such as        reliability that may affect the use of data contributed;    -   contributor scoring: a number or factor that may be generated        based on contributor evaluation and that may be used, for        example, in later interactions with the same member (e.g., as        more or less “trusted”) and/or that may affect a value        attributed based upon contributed data;    -   evaluation of past data submissions: analysis of data, data        sources, contributing members, and so forth based upon        evaluation of historical interactions and contributions of the        member;    -   evaluation of a third-party source: analysis of an entity that        generated or processed contributed data, such as to determine        data quality, completeness, reliability, etc. (such        third-parties may include, for example, sequencing facilities,        medical facilities, etc.);    -   processing of later member-specific contributed data is altered        for trusted contributing members;    -   interacting of the respective contributing member with the        educational interface pages;    -   completion of successive educational modules;    -   compensating contributing members based upon interaction by the        respective contributing member with the educational interface        pages;    -   accessing to the aggregated member-specific contributed data        without permitting third-party identification of members:        activities between the administrative entity and third-parties        to aid in analysis of aggregated data, such as for research and        discovery without relating the aggregated data back to        individual contributing members in a way that would identify the        members;    -   remunerating by/from the third-party: transfer of value from        entities interested in the aggregated data in exchange for        activities such as searching, access, etc.;    -   stages of interaction by the third-party interface: progressive        activities of establishing an account or relationship between        the third-party and the administrative entity, arranging for        remuneration for activities with the aggregated data, etc.;    -   third-party interface is configured to cooperate with the        processing circuitry to perform searches;    -   permitting communication by the third-party to contributing        members without permitting third-party identification: following        analysis by a third-party, allowing certain contact (e.g., via        email) between the third-party and contributing members (e.g.,        for invitation to clinical trials) in a way that does not        provide the third-party with the actual identification of the        contacted members;    -   third-party communicating based upon a unique identifier        associated with the aggregated member-specific contributed data        of the contributing members: similar communication but based        upon technologies where the members are associated with        identifiers that do not allow for personal identification of the        members;    -   opting-out of communication from the third-party: an operation        that a member may perform (e.g., via interface pages) to        preclude being contacted by third-parties;    -   attributing a value to at least some of member-specific accounts        based upon remuneration provided by the third-party for access        to the aggregated member-specific contributed data: channeling        of value (e.g., remuneration) based upon interest in or use of        member data by third-parties, typically through the intermediary        of the administrative entity;    -   attributing the value to at least some of the member-specific        accounts based upon remuneration provided by the third-party;    -   attributing a value is based upon whether the respective        member-specific contributed data corresponds to criteria        provided by the third-party: may relate to specific remuneration        or channeling of value to certain members whose contributed data        is of particular interest to a third-party;    -   selecting a portion (for sandbox) of the aggregated        member-specific contributed data for access by the third-party:        down-selecting some data from the aggregated data that meet        criteria of a third-party, such as resulting from a search;    -   segregating data: data for a given individual being segregated        across several databases to increase security; each database has        a different key for the individual so information cannot be        combined without having all of the keys for an individual        (stored in the segregation key database).

DESCRIPTION OF EMBODIMENTS: Turning now to the drawings, FIG. 1illustrates an example data aggregation and management system 10 at theservice of a member population made up of human members 12. The memberpopulation may be thought of in some respects as “users” to the extentthat they will interact with the system via served interfaces both tocreate accounts, to contribute data, and to manage aspects of theiraccount and data. They will typically comprise human contributors madeup of individual members who may create member accounts and contributedata (typically about themselves) as set forth in the presentdisclosure. The data contributed may also include various populations ortypes of organism for which members may have data, including, withoutlimitation, animal populations, and other populations (e.g., plants,microbes, environmental areas such as water and earth sources).

The system allows for data, files, and records 14 to be accessed, anduploaded for processing and aggregation of their content. In the presentdisclosure, contributed data may be referred to simply as “data” or“files” or “records” interchangeably. As discussed in the presentdisclosure, provisions are made for de-identifying the data contributed,that is, for removing the ability to relate the contributed data back toan identity of the contributing member, unless the member desires andconsents to such identification. Management of the data, the account,and coordination of value attribution is by the system administrativeentity 38 (i.e., the aggregation administrator or coordinator). Theadministrative entity may maintain a platform or system 16 itself havinga number of components and systems as discussed below.

The contributed data may include genomic, or more generally omic data,medical data, personal data, including personal, family, medical andsimilar historical data, medical records, and any other data that may beof use in research and/or analysis of physical states or conditions ofthe relevant populations. These may be in the possession and/or controlof the contributing member, or may be held in trust by variousinstitutions, as in the case of files. In such cases, the members mayaccess the files by physical or electronic transfer, or by any suitablemeans, or may simply make the data and/or files available to the system(e.g., via periodic, episodic, cyclic, or other transfer, such as from“wearable devices”).

A wide range of individuals, institutions, businesses, and communitiesmay create or assist in the creation of the data for each contributingmember, and in many cases, the types of data, the completeness and/orcorrectness of the data may at least partially be a function of theoriginating source. The present techniques assist in completing andcorrecting contributed data, analyzing the data for quality, interactingwith the contributing members to ensure high-quality data, and so forth.In the embodiment illustrated in FIG. 1 , for example, the contributingmember may themselves create or make available certain personal data 18,which could be provided via an online interface, form, template,questionnaire, or any other means. Moreover, various medical,governmental, quasi-governmental, health, and other institutions maycreate data, as indicated by reference numeral 20 (e.g., medical recorddata, hospitalization and treatment data, etc.). Similarly, imaging andlab institutions 22 may create image and related data. Medical officesand medical professionals 24 may create other health-related data (e.g.,via regular or other visits compiled as patient medical records and thelike). Pharmaceutical and research organization may create further data,as indicated generally by reference numeral 26. Omic data, including,for example, sequence data may be created by other contributors, asindicated by reference numeral 28. Many other sources of data may beenvisioned as well, including wearable devices, home test kits anddevices, personal electronic devices (e.g., computers, tablets, cellularphones), employers, just to mention a few. In general, the presenttechniques may focus on health-related data that may be useful to thecontributing member or to others in the contributing community (orbeyond), though that term should be very widely understood insomuch asmany facets of the member's life may affect or be reflected in datapossibly contributable by the respective member.

The system 16 provides a number of services, and these may evolvedepending upon the organizational structure of the administering entity,and the needs and desires of the member community and third-party users.For example, in the illustrated embodiment, these may include an accountinterface system 30, a file/data management system 32, a data storagesystem 34, a value/share attribution system 36. Many other systems mayalso be present, or added, such as a third-party interface system thatmight allow third-parties to make use of the aggregated data, contactmembers, and so forth, such as for a fee.

In the illustrated embodiment, the members will typically interface withthe system via a computer (or any other capable device, such as atablet, smart phone, etc.). Data exchange will be enabled by any desirednetwork connection, so that member data, account data, and contributeddata/files 14 may be provided. Similarly, data exchange may take place,also by any desired network connections, with the contributing membersor users making up the community, as indicated by reference 40, and withany other permitted third-parties 42. Ultimately, based upon thearrangements with these users, value will flow back to theadministrating entity 38 and therethrough to the member community. Manyforms of value may be provided, including monetary payments,cryptocurrency payments, ownership shares, and so forth.

As noted in the present disclosure, in some currently contemplatedembodiments, interactions between the community members and theadministrating entity may be based upon smart contracts, as areinteractions with the third-party users. Moreover, the ownership andvalue attributed to the community members may be based upon one or moreencrypted, decentralized, and/or public ledgers, cryptocurrencies, andso forth. Such techniques may allow for reliable tracking and“transparency” in transactions, while the present techniquesnevertheless are based on confidentiality and member control ofpersonalized or identity-permitting data and data associations.

FIG. 2 diagrammatically illustrates an example of account initiation andmember interaction processes in accordance with certain presentlycontemplated embodiments. The process may begin with the member 12interacting with a personal computer 46, or other device that caninteract with the internet. Interface screens are served to the membercomputer by interacting system components, such as an account portal 48and a member portal 50. The account portal is provided for interactingwith the member computer in ways relating to the member account. Asnoted below, various approaches, protocols, and processes may beimplemented to generate and account for value or shares in the databaseor databases of the present system. The account portal computer orcomputers 52 may include one or more interfaces 54 designed to permitinteraction with the member computer as well as one or more processors56 and memory 58. The memory will typically store various screens andinteraction protocols that are implemented by the processor via theinterface. The account portal may communicate again by any suitablenetwork or combination of networks, and may operate based upon, amongother things, a shares or account API 68.

The member portal 50, like the account portal 48, is maintained oroverseen by the administrative entity of the system. The member portalitself may run on any suitable type of computer or combination ofcomputers 60 and will be in communication with the member computer bythe Internet or any suitable network or combination of networks. Themember may contact the portal by a conventional URL, or by a browsersearch, or any other initial contact mechanism. The interface screenswill walk the member through the account creation and data transferprocess. As will be appreciated by those skilled in the art, thecomputer system running the member portal will typically comprise one ormore interfaces 62 designed to allow for data exchange between theadministrative entity site and the user computer. The interface 62 is incommunication with one or more processors 64 and memory 66. The memorymay store the interface screens, routines for generating the interfacescreens, routines for processing member data, and so forth, theseroutines being executed by the processor. The member portal 50 is incommunication with and executed based upon a member API 72.

Data received, processed, analyzed, stored, and otherwise handled by thesystem, including both account-related data, member-specific contributeddata, and aggregated data may be stored in one or more data storagesystems 70 and 74. As discussed below, in the present context, these maybe referred to as “centralized databases”, meaning that they areavailable to the administrative entity for data access, analysis,searching, and similar operations. In practice, such databases may bephysically stored in different locations, and may technically comprise“federated” databases, or any desired storage or data structure may beused.

As noted above, interaction with the administrative entity may be basedupon one or more smart contracts as indicated by reference 76 in FIG. 2. Such smart contracts may detail and/or manage various interactions,stages of interactions, responses to interactions, and may keep reliableand traceable records of interactions with the members. In presentlycontemplated embodiments these interactions will be noted on ledgerentries as indicated by reference 78 in FIG. 2 . As also shown in FIG. 2, data storage devices or systems 70 and 74 (again, comprising one ormore “centralized” databases) will make the member-specific contributeddata, data derived from it, and/or the aggregated data available to adata analysis engine 80 which may examine the data to determine suchfactors as data quality, data completeness, data consistency, dataaccuracy, and so forth, but that may also analyze the data to determinepossible follow-up with contributing members, possible “cohorts” ofmembers who share one or more traits, conditions or issues, and soforth.

FIG. 3 illustrates various example data types that may be contributedand handled by the system and processes. As noted, any of a wide rangeof data types may be contributed, processed and stored. It may be notedhere that while reference is made in this disclosure to “types”, inreality, many of these may be super- or sub-sets of one another, and insome cases, files and other collections of data may include severaltypes. But for the present purposes, it is useful to consider these asseparate types for many reasons, such as the fact that they oftenoriginate from different sources, they may comprise different file anddata structures, they may be reported at different points in time, theymay tend to include characteristic incompleteness and inaccuracies, theymay be initiated at different times and in different ways, or simplybecause they may be thought of and handled by the members differentlybecause of the underlying reason for their creation.

In the illustration of FIG. 3 , data, generally referred to by numeral82 may include different data types are indicated by reference numeral84. These are illustrated as including different specific types of data86, such as demographic data, encounters (e.g., between individuals,with environments, with pathogens, with diseases, etc.), diagnosticdata, medical procedure data, medical follow-up data, omic data, socialdata, lifestyle data, environment data, exercise/activity data, and dietdata—though clearly these and many other data types may be considered.In general, such data may be compiled, stored, and even contributed tothe system as structured data 88, or unstructured data 90. In theillustration, the structured data is shown as including specific typesof files or data 92, such as survey inputs, health records, patientrecords and reports, and sequence or omic reports. Here again, manyother types of contributed data may be in a structured format. Otherdata which is unstructured may nevertheless be of keen interest,particularly when considered together with structured data, such asimage data, physician notes, personal contact data, as well as manyothers. As noted in this disclosure, for example, lifestyle details,automatically detected data (e.g., from worn or home devices), and soforth may be structured or unstructured, and can still be handled by thesystem.

FIG. 4 illustrates analytical ways in which such data may relate to acontributing member, and presents a different way to consider thecontributed data. In this example, the relational structure 96 maycorrelate data 98 of an individual (or community) by, for example, datatype and the one or more sources or methodologies for its creation andcollection. In the illustration, the data 98 includes an environmentallayer 100, a phenotype layer 102, and a genotype layer 104. Each layermay give rise to different data types 86 as discussed above, and thesemay be implied by the characteristics of the respective layer or layers(e.g., environment data may include encounters, geographics,demographics; phenotype data may involve ethnicity, physicalcharacteristics, family details and history, personal data, and soforth; genotype data will typically include sequence data, etc.).

Moreover, each of these layers, and the corresponding data types mayimply different data sources and capture methodologies 106. For thepresent purposes, it should also be pointed out that these are alsomechanisms for filling in missing or incomplete data, for correctingerroneous or inaccurate data, and more generally for enhancing data byacquiring and contributing additional data. By way of example, suchsources and methodologies may include medical visits, compilations ofmedical and health records, surveys and questionnaires (including onlineforms and interfaces), sequencing, imaging, wearable devices, home testdevices, just to mention a few. Here again, these may produce structuredor unstructured data (or both).

FIG. 5 illustrates different data types and their inter-relation, andways in which gaps in data may be identified and filled, or incorrect orinaccurate data may be corrected. In this illustration, the data 108originates from sources 110. The data may be thought of, similar to thedepiction of FIG. 4 , as different layers or data types 112. In mostcases, the data contributed (or available) is not exhaustive in eachlayer or type, as indicated by the regions, holes or gaps 114 and 116(which could also represent inconsistencies, inaccuracies, or lowquality in the data). Such regions may be identified in a variety ofways. For example, analysis by the analysis engine of the processingcircuitry disclosed may identify that certain fields or inputs aremissing or inconsistent with one another in data of a particular type,or in data contributed at different times. In the illustration,moreover, conceptual or analytical pathways 118 and 120 through the datamay reveal that useful parts of each layer or type are missing orinaccurate (or that a layer or type is missing entirely). As discussedbelow, the system may be able to automatically fill in the missing data,and such fill techniques may be based upon artificial intelligence,machine learning, simple comparison between similar data fields, and thelike, so that the system is progressively refined and may require lesscommunication with contributing members. In other cases, communicationsmay be initiated with the members (including fully automatedcommunications) to acquire or re-acquire data as indicated by reference122. The requesting communications 124 may take the form of emails,customized surveys, requests, recommendations, and so forth. Inpresently contemplated embodiments, such communications are“de-identified” such that the administrative entity does not identifythe contributing member by association with the contributed data.

FIGS. 6A-6C illustrate certain example technologies or workflows foracquiring certain data types, and then for completing or complementingthe data over time. FIG. 6A illustrates certain data originatingtechnologies 126 for sensing or monitoring bio-related data for amember. These will typically be based upon the type (or sub-type) ofdata involved, and sometimes on the anatomy or physiological system fromwhich the data is taken. For example, data may be collected relating tothe brain 128 and brain function, typically from electroencephalograms(EEGs) and similar studies, but also from wireless mobile EEGs. Datapertaining to the eyes 130 may be collected by medical exams, eyeglassprescriptions, medical procedures and surgeries, and so forth, but alsofrom glucose-sensing lenses, digital fundoscopes, smartphonevisual-acuity tracking, automated refractive error measurements,non-invasive intraocular pressure detection. Head-related data 132 maysimilarly be obtained by physical examination, but also by seizurerecords, autonomic nervous activity detection, head-impact sensors,non-invasive intracranial pressure detection, and voice or respiratorystress recognition. Cognition data may be collected through onlinetests, gamified evaluations measuring mental or physical responses, orthrough the continuous or episodic tracking of keyboard stroke pressurevariation, timing, and other patterns during any typing activity.

Much data 134 relating to body organs and organ systems may be acquireddifferently based upon the particular physiological structure, includingcontinuous blood pressure tracking for the heart and vascular system,handheld or wearable electrocardiography devices, heart rhythmdetection, cardiac output detection, stroke volume measurement, andthoracic impedance measurement. Information on the lungs may bedetected, for example, by home spirometry, pulse oximetry, inhaler use,breath-based diagnostics, breathing sounds, and environmental exposure.Regarding the blood, data may be obtained from continuous glucosemonitoring, transdermal detection, genomics-based pathogen detection,and blood tests. Skin-related data may be obtained by monitoringtemperature, lesions, pressures (e.g., for wound care), sweat chemistry,and cutaneous blood flow. Bladder and urine information may be had fromcomprehensive urinalysis, sexually transmitted disease tests, and forinfants, diaper-based sensors. Moreover, gastrointestinal data may beobtained by endoscopic imaging, esophageal pH monitoring, fecal bloodand bilirubin tests, gut electrical activity, and so forth. Othersystems, including the skeletal system, the endocrine system, and soforth may be related to other acquisition technologies andmethodologies, including many types of medical imaging.

In all cases it is anticipated that additional, refined, and newmeasuring methods, techniques, and devices will be developed over timethat were not either available or practical at the time of the presentdisclosure. The methodologies and techniques outlined here maynevertheless be adapted to such evolving technologies.

In general, some of these will clearly entail the help of providers,including medical professionals, laboratories, and the like. Others maybe based upon direct input by the members (e.g., via the systeminterfaces, questionnaires, surveys, etc.). Still further, andincreasingly, data may be obtained continuously, periodically, orepisodically by personal and/or wearable devices, as indicated byreference 136. Such data may include, for example, pulse, bloodpressure, temperature, activity, hydration, sleep stages, seizuredetection, respiration, oxygen saturation, blood chemistry, ECG, cardiacoutput, stroke volume, stress, and so forth. Similarly, where available(and permitted by the member), location, movement, interests, treatmentcompliance, and similar data may be collected from online activities,cellular and mobile device records, and so forth.

FIG. 6B illustrates an example data originating workflow, such as formedical-related data. In many instances, this workflow 138 will beginwith an individual 12 visiting a medical provider or institution 110.Based upon the visit and the activities, tests or treatments performed,a record 140 is produced (or typically many such records). These may bemade available to the individual (patient), who will then be or become acontributing member. The record is digitized as block 142, either by theprovider or upon submission to the system, and one or more structureddata/files are produced. These are then stored as indicated by reference146. As indicated by the arrows returning to the left in the figure, atany of these stages the member or system may loop back to obtainadditional data, augment the data, contribute missing data, correctincorrect data or the like. The arrows also indicate that many suchloops may be made over the course of the lifetime of the member, and allsuch data may be collected and processed to enhance the value providedto the member, and its use in the service of the particular member andthe community.

FIG. 6C illustrates another example data originating workflow, such asfor omic data. In this workflow 148, a sample from any subject ofinterest may be obtained that may comprise omic material. These mayinclude, for example, the member 12, but also any environment 150 towhich they are exposed, any pets or other species 152, or moregenerally, any relevant source of omic material (e.g., saliva, gut,feces, blood, hair, organ and tissue samples, specimen swabs, etc.). thesample 154 is then processed, typically by a genomic laboratory asindicated by block 156 to produce one or more files containing extendedsequences of reads, lists of genomic variants, identified pathogens withantibiotic resistant properties, microbiota presence and abundance, orany other method of conveying detailed or summary information relatingto the omic tests conducted. In practice, processed data or summarizeddata may be provided rather than raw data, and this may be in the formof tag data, text files, or written reports. This data may becontributed, and processed, as indicated at block 158, then stored atblock 160. Here again, at any stage of the data acquisition orprocessing loops back may be useful to complete or correct data, or theprocess may be repeated one or many times (for the same or tissues) overtime.

FIG. 7 is a diagrammatical representation of an example interplaybetween certain data originating and acquisition approaches, and aprocessing and analysis system that may be maintained and operated by anadministrative entity. This figure is intended to depict that the systemand methodology may be very interactive and interdependent, with databeing contributed, received, analyzed, corrected, completed, and used inmany different ways, with communications back to the members. In fact,it is anticipated that the ways in which the data is analyzed and usedwill evolve over time, such as when new conditions are recognized, newdata is available, new data types and acquisition techniques aredeveloped, new diagnoses are made, new cohorts are determined, newsymptoms develop, and so forth.

In the illustration, the processing 162 may be thought of as centeredaround the processing system 164, which itself includes the analysisengine. This analysis engine will generally comprise computer code andalgorithms operating on local, internet, or cloud based hardware, thatmay itself have one or more “modules” and that may be progressivelydeveloped, refined, and expanded over time. It may be specificallyadapted to handle different file types, data types, formats, and soforth. It may also be specifically adapted to identify specific types ofgaps and errors in data, to attempt to fill the gaps or correct theerrors based on other data of the same type, other data of differenttypes, other data of other members, other data from the same memberprovided at different types, and so forth. Some gaps and errors may bequite simple (e.g., a mismatch between a zip code and a city), whileothers may be less intuitive (e.g., the name of a diagnosing physician).Some errors may entail a simple misspelling, while others may requireclarification by the member, or more detailed analysis of past recordsand data for auto-correction.

Such exercises and analysis may be performed at any desired stage ofoperation of the system. In many cases, initial analysis, completion andcorrection (with or without communication to the member) may beperformed upon or soon after submission of the data to the system. Inother cases, analysis may follow submission of additional data of thesame or a different type by the same member. Still further, the analysisof one member's data may follow from submission of data from others thatmay correspond in some way to the first member's data (e.g., relating toa common condition, situation, possible diagnosis, possible healthactivity, environmental exposure, possible legal claim, etc.).

As shown in the figure, survey data 166 may be provided, and in manycases the first data provided by a member may comprise such data (e.g.,geographic information from a home address, simple question/responsedata). The arrow back and forth to the processing circuitry (andanalysis engine) is intended to indicate that the system may requestadditional data, corrections of the data, completion of the data,verifications of proposed corrections and completions, and so forth,which in some cases may be done during a single data-submission session.Also illustrated is wearable/tracking data 168, which may include any ofthe technologies and methodologies discussed above. Here again, thecorrection and completion process may be interactive based upon analysisof the submitted data. Consultation, exam, and similar data 170 may besimilarly submitted, and analyzed for completeness and accuracy.Finally, follow-up data 172 may be submitted. Such data may include manydifferent types, including survey data soliciting feedback on whethertreatment plans are followed, results of treatment, response of aphysical condition to treatment, and so forth. But it should be notedthat none of these inputs or data types is limited to occurrence of adisease or condition, but much more generally, they may simply relate tothe health and wellbeing of the member.

The figure also represents that over time, and based on variousinitiators, the system, based upon analysis of contributed data of themember or other members, may interactively solicit completion orcorrection of contributed data, such as by recommending additionalexams, tests, treatments, or simply the updating or supply of other datathat may be of use on improving the life and wellbeing of the members.

FIG. 8 represents an example manner in which such data originating,acquisition, analysis, completion and correction approaches may comeinto play in a “patient journey” addressing a member physical condition.The term “patient” here should not imply that the member has or willhave any particular disease or medical problem, but simply that theprocess relates in some way to health. In some cases, the “patientjourney” will relate to a chronic, acute, or progressive disease ortreatable condition, while in other cases it may imply some portion oraspect of the member's life (e.g., normal care stages includingprenatal, following birth, vaccinations, standard check-uprecommendations of a healthcare system, eye care, regular checkups,regular monitoring, diet management, exercise regime, etc.).

As shown in the figure, the patient journey will in many cases, beginwith awareness or screening (or some sort of exam, personal realization,or interaction with a medical or other provider), as indicated at block176. Such events may include, for example, the development of acondition, symptoms, or the like. But they may also include “normal”life events, such as a birth, initiation of a personal program (e.g., adiet, a workout program, vaccinations, beginning of school, a physicalmove to a new location, initiation of a self-interested program toacquire omic data, desire to start a family, etc.). Such events maycreate various data 178, some of which can be provided via online tools,questionnaires, surveys, and the like. Other data may comprise medicaltests, exam records, wearable device data, and so forth. Any or all suchdata, whether initially structured or unstructured may be submitted tothe system (managed by the administrative entity) for management,storage, processing and analysis (including aggregation, automated andhuman-assisted correction and completion, etc.) by the techniquesdisclosed.

At some point, a diagnosis 182 may be made of some condition, disease,or health or lifestyle concern. Such diagnoses may be made in anysuitable manner, such as by conventional medical professionals followingvisits, but also via remote medicine (e.g. telemedicine), onlinemethods, and certain artificial intelligence and machine learningtechnologies. In many cases, the member will seek assistance of healthor medical professionals, resulting in additional records, data, images,and so forth, as indicated at 184. In some cases, these may be basedupon recommendations by the system, as discussed below. This data maythen be submitted and processed as well, and analysis may allow forcompletion and correction.

Later, therapeutic options 186 may be explored or made available to themember. These may address specific diagnosed diseases or conditions. Butmore generally, “therapeutic” should be read to include steps oractivities more generally. By way of example, a pregnant member mayconsider changes to diet, physical activity, and work schedules. Anewborn may benefit from certain types of monitoring, developmentaltracking, diet, and so forth. At later stages of life, the member mayconsider a wide range of lifestyle and other options to address anyparticular personal concerns. This data 188 may be submitted to thesystem, and automated or assisted completion and correction may beperformed based upon analysis.

Stage 190 represents treatment of a condition or concern. This mayinvolve an actual disease, such as via surgery, prescription drugs, orany other procedure. More generally, it may entail any steps oractivities actually taken by the member with or without outsideassistance. Data 192 relating to the actual regime adopted may besubmitted, analyze, completed and corrected as disclosed. Suchinformation may be extremely useful for determining how the activitiesaddress any condition or concern which caused the member to considerthem, particularly when considered in connection with data on conditionmanagement, as indicated by stage 194. Together, such data may indicatewhether treatments are actually followed, whether recommended medicationregimes, diets, lifestyle changes, and the like are followed. Conditionmanagement data 196 may often include survey data, but may also includemedical follow-up reports, lab test results, wearable device data, andso forth. Additionally, data will include information on patient relatedoutcomes and the patient related experience from the treatment, as wellas longitudinal data collected months and possibly years after theinitial diagnosis and treatment.

In the illustration, the patient journey is completed with patientempowerment 198. The ultimate goal of such activities (and datacollection and analysis) is wellbeing of the individual. But this mayinclude benefitting from the journeys of others, and allowing others tobenefit from the member's experiences. In the illustration, this stagemay produce very interesting data, such as improved determination ofcohorts who may share similar experiences. Moreover, it may allow theindividual to “loop” back in various ways for the same or a differenthealth consideration. For example, based upon the benefits ofinteraction with the community and the system, the member may considercloser cooperation with the data contribution process.

The present techniques, platform, and processes offer many uniquebenefits both to the members and to the community at large based uponinteraction during the patient journey. First, it is contemplated thatthe actual share or value attributed to each member will be increased bythe contribution of more and better data. It should be kept in mind thatthis may entail one or many cycles through the same or different patientjourneys for each member, with the same or different initiators and thesame or different steps. The analysis of the member-specific contributeddata, the aggregated data, and analysis of both together may allow thesystem to determine and recommend data acquisition, completion andcorrection based upon similar journeys by other members. Manyrefinements may be realized as a result of the analysis and dataenhancement, including refinement of aggregated data; refinement ofrecommendations due at least in part to the data reflecting similarconcerns, activities and outcomes of others; refinement of efficiency ofthe journey and steps taken (e.g., by recognizing through data analysiswhat steps are more or less effective for comparable members withcomparable concerns); refinement of screening and prevention (e.g., bythe ability to recognize in the member-specific contributed data and theaggregated data potential risks of conditions); identification ofpreviously unrecognized conditions (e.g., new diagnoses recognized bythe medical community and determinable by the contributed data); andrecognitions of personalized medicine correlations (e.g., based upondeep understanding of the makeup, lifestyle, demographics, etc. of themember, what approaches are most effective based upon the experiences ofothers).

FIG. 9 illustrates certain example operations 202 that may be consideredin processing via the components of the foregoing figures. The processof account creation, indicated generally by reference 204 may begin witha prospective member accessing an online tool as noted at operation 206.This online tool may take the form of a screen or screens that permitinput of data and provide directions and information to the perspectivemember. At operation 208, then, the prospective member creates anaccount and this account may be verified, such as by verifying an emailcontact for the member. At operation 210, then, a unique member ID iscreated. Importantly, this member ID may be used for all memberinteractions with the system, and is a part of the basis for separatingindividual or personal data from the data uploaded for aggregation. Thatis, respecting member anonymity or confidentiality, the unique ID allowsfor many types of member interaction with the system while maintainingseparation between the aggregated data or files and the personalidentification of the member. The member idea may be encrypted locallyon the member computer using member login information, such that it isnot directly linked to the member's account until it is unencrypted.

At operation 212, then, any information provided by the new member isstored, and at operation 214 identifying information is separatelystored. It may be noted that through all of these operations, and basedupon the protocols set forth in the smart contract, quality control andother required operations or milestones may be performed as indicated byblock 216. For example, when data is uploaded the smart contract maycall for a quality control operation on the data and a response may bedefined, such as receiving a quality control metric, as well as anaction may be taken, such as to compare the metric to a pass/failhurdle, to make a pass or fail decision, and so forth. Responses mayalso be defined at such steps, such as indicating to the user whetherdata is acceptable or not, whether data or files pass or fail, and, forexample, if the response is a “pass” the data may be entered into thedatabase, shares in the database may be allocated, entries may be madeto a ledger, and so forth as described below. Similarly, in the case ofa “fail”, actions may include placing data into a failed data queue,informing the user, making a leger entry, and so forth.

It is also contemplated that the member may have direct access tocertain data and files, and in such cases, may upload the data or filesdirectly. In other cases, the member may instead provide links to dataand files that can be the basis for access by the processing systems ofthe administrative entity. In yet other cases the members may fill out asurvey and the data would be extracted from the answers directly orafter quality control testing and other processing.

FIG. 10 illustrates exemplary logic for providing transparentconfidentiality in processing of member data and files. As notedthroughout the present disclosure, an important aspect of the system isthe ability to reliably trace interactions with the system, and betweenmembers and the system, as well as third-parties in the system. Suchinteractions should not only be transparent and reliably traceable, butshould also respect the confidentiality of the participants, andparticularly of the community members. Various processed may beenvisaged and implemented to provide both the desired tracing andtransparency needed for reliability, as well as member confidentiality.In general, this is done by separation of member identifying data fromuploaded data in files. The latter becomes de-identified data whichcannot ordinarily be associated with the identity of the contributingmember. Nevertheless, the system allows for the account to be created,augmented, and for value (e.g., remuneration) to be passed along to theparticular members based upon third-party utilization of the database ordatabases.

In the implementation illustrated in FIG. 10 , this process 218 againbegins with the uploading of data or files as indicated by reference220. When the data is uploaded it is stored as indicated by reference222 and as discussed above. The data may typically be stored at astructured data location as indicated by 224. Moreover, this processagain begins a protocol in accordance with smart contract processing asindicated by reference 226. Though not separately illustrated in FIG. 10, it should be borne in mind that this smart contract processing mayinclude individual stages or toll gates that are passed, and each may beassociated with actions, responses, notifications, and so forth, all ofwhich are recorded in one or more ledgers.

At block 228, the processing invokes a universal resource identifierprotocol (URI). Such protocols may be crafted to provide restrictedprocessing of the data stored at location 230. For example, in apresently contemplated embodiment, the URI protocol will requirecredentials which may be embedded into queries made by theadministrative entity. Accordingly, such queries may be the basis forthe processing performed by the administrative entity, and because it isexceedingly unlikely that such credentials could be reproduced by otherentities, the URI protocol ensures that only such queries will meet therequirements for response. Moreover, in the presently contemplatedembodiment illustrated, a limited number of uses may be made of the datain accordance with the URI protocol as indicated by reference 232. Inthis contemplated embodiment, a single use is permitted. Further, inaccordance with this embodiment, a restriction on the duration orlifetime of the availability of the data or URI is made as indicated byreference 234. Once this time expires, the queries are no longerpermitted and the process must move to an earlier stage, possiblyincluding re-uploading of the data.

The figure also illustrates the separation of subsequent operations. Forexample, based upon processing, and as discussed above, data and filesare stored as indicated by reference 236. In a separated way, however,user accessible data is updated as indicated by reference 240. That is,the user account information, value or shares attributed to the user,and so forth may be accessible to the user, while the same informationis not accessible to the administrative entity, owing to the separationof the data and files stored at block 236 from the user informationaccessible at block 240. As indicated at 238, however, the user identityand uploaded data, files, and share information may be linked so thatattribution may be made, and remuneration passed along to the membersbased upon the uploaded data and files, and their utilization bythird-parties.

FIG. 11 illustrates example processes 242 presently contemplated foruploading, receiving and processing member data files. The processes maybegin with the uploading of data or files as indicated at block 244. Asnoted above, all search processes may be performed in accordance withprotocols established by one or more algorithms or smart contracts (or“smart code” or other suitable immutable and traceable protocol) asindicated at 76 in FIG. 11 . Each stage executed may include initiatingactions, receiving responses, and taking actions based upon receivedresponses. For each of these steps or stages in execution of the smartcontract, and based upon the interactions between the system and themember, ledger entries are made as indicated at reference 78 to maintaina reliable record of the interactions. Though not separately illustratedin FIG. 11 , such smart contract stages and ledger entries are made ormay be made at all of the various steps of processing.

The uploading process transfers data or a file 246 to one or moretemporary storage systems 248. Temporary content storage, as indicatedmore generally by reference 250 may store unprocessed or partiallyprocessed data or files waiting in a queue for other actions, such asquality control. Individual files 252 are then transferred by a qualitycontrol broker 254 for one or more types of quality control. In certainpresently contemplated embodiments, structured data or files may beconverted or processed to make them more structured, understandable,comparable, searchable, or to facilitate extraction of data foraggregation. Such files, as indicated by reference 256, may betransferred to a converter cluster 258. Genomic data files, as well asany other contributed data or files, for example, may be most usefulwhen placed into a structured and standard format or when stored basedon comparison to a reference file. Converter cluster 258 may provideprocessing for creating congruent files, verifying that the files relateto a particular population, species, individual, and so forth, forformatting the files and contents of the files and so forth. Where suchprocessing or conversion is not desired or required, the files may bepassed to a quality control process content storage 264 as indicated byreference 262, or the converted or processed files may be similarlyplaced in the quality control process content storage as indicated byreference 260 in the figure.

Files waiting in a queue in the quality control process content storagemay be individually transferred, then, as indicated at 266 by a qualitycontrol broker 268 to perform validation on the files. The files sentfor validation, indicated at reference 270 are considered by avalidation cluster 272. Validation of data or content of such files maybe performed based upon the type of data in the file, expected aspectsof the data, standard data to which the processed data may compared, andso forth. For example, the validation cluster may check for redundancyor near redundancy (e.g., a member has uploaded the same data more thanonce, or copied a file and has made one or few changes, commonality ofvariants (e.g., the member has uploaded inconsistent files),verifications versus reference data (e.g., genomic data compared tohuman or other species reference genomic data), statistical analysis ofthe data, and so forth. The validation cluster may produce a validationor analysis report. Thereafter, the validated or processed files 274 maybe transferred to a validated data storage 278. If such validation isnot desired or required, the files may be forwarded to the storage 278bypassing the validation cluster, as indicated at reference 276.

Individual files may then be extracted from the validated data storageas indicated by reference 280 to a data analysis engine 282. Inparticular, various types of quality, credibility, reliability,completeness and aspects of the data may be measured, and scores may beattributed that may be used for various purposes, including, wheredesired, attribution of shares or value. Other types of analysis,discussed in the present disclosure, may include, for example,determination of useful follow-up communications with members,recommendations for further data acquisition, tests, and so forth,determination of possible membership in cohorts based on the data, andmany other processes.

A credibility or data quality score or report may be generated atoperation 284. Based upon such scores, certain members may be designatedas “trusted” or reliable members, and later processing of contributionsby such members may be altered, such as by alteration of certain qualitycontrol applied to the data or files, or value attributed to the data orfiles based upon the quality and/or reliability of the underlying data.At this or a later stage, the system may analyze data of the same or adifferent type, or data received at different times, and attempt tocomplete or correct the data, as described below. Other operations, suchas creation of a customized template or survey may be performed, such asto solicit the contributing members to complete or correct contributeddata that appears to be incomplete or inaccurate. When the data iscompleted or its accuracy (or more generally, its quality) is improved,such improvements may be reflected in increases or changes in the valueattributed to the contributing members. The score may be saved and beused later as part of a statistical analysis evaluating the overallquality of all the data provided by a user or the statistical confidenceof conclusions in a study across multiple users. At operation 286, theanalysis engine may also determine that the processing of the data issuccessful or that a failure has occurred, requiring eithersequestration of the data, partial acceptance of the data and so forth.At operation 288, then, value, ownership increment, profit distribution,or shares may be attributed to the member based upon the data. Anysuitable formula for attributing value may be applied at this stage, anddifferent formulas may be developed as different types of data and dataof interest are determined and provided by members.

Finally, as indicated at references 290, 292, 294 and 296, the data andfiles are stored. In presently contemplated embodiments, these arestored in separate storage spaces, with genetic files being stored in afirst storage space 292 and medical and similar data and files beingstored in a storage space 296. Of course, each of these storage spacesmay comprise one or many different physical storage media and locations.As noted above for all of the steps and based upon the smart contractprotocol implemented, ledger entries are made as indicated at reference298, and notice is provided to the members of the processing and valueattribution as indicated by reference 296.

As noted above, various approaches and formulas may be used for theattribution or allocation of shares or value based upon the data andfiles provided by members. Exemplary processes for such value or shareattribution may begin with evaluation of the type of data provided bythe member, such as personal data, medical data, health records, historydata, omic data, or any other type of data. The system may then performanalysis and quality control on the data as noted. Many other factorsmay be considered that can be incorporated into the evaluation,particularly completeness, consistency, and so forth. Such completenessand consistency may be analyzed within and between data and filescontributed by the same member at a single time or between datacontributed at different types. Similarly, completeness and accuracy maybe determined between data types (e.g., to ensure that data of one typeis consistent with data of another type). Data may also be analyzedversus similar data (e.g., data of the same or similar type) contributedby other members, or even aggregated data for many members. Further,where standard data (e.g., reference omic data, reference disease statedata, etc.) is available comparisons may be made with such references.Complex computation of shares or value for individual members and forindividual data may be made by the system. In general, the sum of allvalue attributed to the individual member can be applied regardless ofthe number of times the data is added, altered, supplemented, removed,and so forth. Based upon factors such as the completeness, accuracy,consistency, quality, reliability, veracity, and so forth, then, theshares calculations may be applied. The calculation could be a simplescore used to accept or reject contributed data or the score could beused to weight the data value and subsequently the share allocation. Asalways in these processes, where smart contracts are utilized, a ledgerentry may be made and the member may be notified.

FIG. 12 is a flow chart illustrating an example data gap or erroridentification and correction process 302. As noted above, upon receiptof member-specific contributed data, the analysis engine determinewhether the data is incomplete or incorrect, and attempt to completeand/or correct the data. This may be done on the data in either astructured or unstructured form, although in some cases the data may beconverted to a structured form that is more easily analyzed prior toanalysis for incompleteness and errors. In the illustrated embodiment ofFIG. 12 , the data is structured and processed at operation 304. One ormore analysis routines or programs may then be invoked and executed, andbased upon such factors as anticipated fields, desired fields, textualand/or contextual analysis, the system determines whether any gaps,incompletions, or errors are present in the data at operation 306. Ifany are detected, the system may attempt to provide the missinginformation or to correct the errors, as indicated at 308. Simple errorsof spelling, dates, names, addresses, and so forth may be provided byreference data, or by comparison of the contributed data to othercontributed data from the member, or from data of other contributingmembers (e.g., in a similar location, with the same or similarproviders, etc.). Artificial intelligence and machine learningtechniques may also be employed, such as to infer the likely missing orcorrect data from other data for the same or other members. It iscontemplated that over time, such techniques will learn patterns andtypical gaps and errors, both for the same and other members.

If such attempts do not provide all of the desired data, the system maycommunicate with the contributing member to solicit additional orcorrect data as indicated at operation 310. It is contemplated that suchcommunications will be made in a “de-identified” manner, wherein theidentity of the member is not available to the administrative entity. Atthis point, or if no gaps or errors were identified, the data may bestored, as indicated at operation 312. Even in such cases, the analysisengine may determine that, at least partially based on the contributeddata, other data recommendations may be desirable. Such determinationsmay be based, for example, on analysis of conditions or health concernsthat may be detectable by analysis of the contributed data, includinganalysis in combination with other data types from the same member, orearlier-contributed data of the member. Moreover, such recommendationsmay be based upon analysis of a patient journey of the type describedabove. They may also be based upon aggregated data of other members,particularly members with correlations to the contributing member.Further, such recommendations could be based upon newly identifiedconditions, diagnoses, medical innovations, development of new tests andscreening techniques, and so forth. It is contemplated that the routinesexecuted by the analysis engine will evolve and develop to accommodatesuch new comparisons and recommendations. If no further recommendationis identified, the value attributable for the data may be noted andnotice provided to the member at operation 316.

It should be noted that, though not separately indicated in FIG. 12 , itis contemplated that each operation will result in one or more entriesinto an immutable ledger or tamper-evident ledger in accordance with asmart contract or smart code protocol. Moreover, as noted above, theanalysis and processing may be accompanied by determination of a dataquality score or similar evaluation, which may serve at least partiallyas the basis for the value determination. Many different approaches maybe made so such scoring, including higher scores for more complete oraccurate data, higher or lower scores determined by comparison of thecontributed data to similar data from other members (e.g., as gauged bythe aggregated data), and so forth.

If more data is desired at operation 310, or any recommendations areavailable at operation 314, the communication mentioned above may becreated at operation 318, and a de-identified notice sent at operation320. In general, it is contemplated that such communications maycomprise an email, message on an internet login page, or othernotification and an automatically populated survey, template, form,document, or other online tool that prompts the members to provide,correct, or confirm the data of interest. It should be noted that, asindicated by reference 322, at this or any other stage in theprocessing, the contributing members may “opt out” of variousoperations. This is particularly the case for notifications and requestsfor additional data, or recommendations. For example, some members maynot desire to know of potential conditions or concerns, regardless ofthe potential for additional shares or attributed value. A fundamentalprinciple of the system is to maintain member confidentiality and torespect their desires with regards to the use and analysis of thecontributed data.

FIG. 13 is a flow chart illustrating an example process for recommendingdata gap filling, complementing, and completion. As noted, the follow-upcommunications with the members may entail recommendations foracquisition or communication of more complete or complementary data. Theprocess 324 for such recommendation may allow for assisting the membersin this way. That is, when gaps, errors or recommendations aredetermined, as indicated at operation 326, the analysis engine maydetermine the best method for obtaining the data, as indicated atoperation 328. Many different types of data may be used in this process.For example, the system may determine providers that the member alreadyuses or has used in the past. It may also consider providers in themembers geographic area. Still further, where available, the system mayaccess information on candidate providers, such as ratings, reviews,cost data, insurance plan data, and so forth. Such data may includeconsideration of results (e.g., data quality) experienced by othermembers, the form, format and completeness of the data typicallyavailable from providers, and so forth. Based upon such considerations,one or more providers, protocols, locations, and so forth may becommunicated to the member as indicated at operation 330. Here again, atindicated at 332, the system may allow for opting out of this process orof the notification of operation 330.

FIG. 14 is a flow chart illustrating an example process for identifyingpotentially useful new data for members, and recommending follow-up. Theprocess 334 may begin with an ongoing (e.g., continuous, periodic) orepisodic follow-up initiation as indicated by reference numeral 336. Itshould be noted that follow-up communications may already be made tosolicit or collect the provision of completing or corrected data asdiscussed above. The illustration of FIG. 14 relates to further or latercommunications that may be made for other reasons, on a regular or “fromtime to time” basis.

Any of a wide variety of events may initiate the follow-upcommunications. In the illustration, for example, follow-upcommunications may depend upon determination of a new cohort that couldinclude the contributing member, as indicated at 338. Further, whencontributed data of the member, aggregated data, and/or contributed dataof other members indicates that a new discovery is available,communications may be initiated to members that may benefit from thediscovery, as indicated at 340. Such discoveries may comprise, forexample, determination of a potential new condition, diagnosis, relationbetween data and a potential patient journey, and so forth, to mentiononly a few. Similarly, upon the development of new technologies, asindicated at 342, members may be notified. Such technologies mayinclude, for example, new methodologies for collecting data, new devicesavailable for acquiring data, and so forth. Here again, many other basesmay be considered for initiating such communications.

At operation 344, then, the member-specific contributed data and/or theaggregated data is searched to determine members who may benefit fromthe communications. At operation 346, those members are determined(albeit in a manner that avoids identification of the individuals by theadministrative entity). And at operation 348, notifications may be sentto those members, such as via a de-identified email. It should be notedthat in all of these operations, the members may opt-out ofconsideration, as indicated by reference numeral 350.

As noted above, the communications are de-identified so that theindividual members are not personally identified to the administrativeentity, to researchers, or when applicable, to other members. Moreover,as summarized above, where suggestions or recommendations are made forthe acquisition or contribution of additional data, such recommendationsmay be advantageously refined by identification of “best” locations,providers, and so forth.

The processes outlined in FIGS. 12, 13, and 14 may be used incombination, and in some presently contemplated embodiments, certainpatient journeys may be the basis for “templates” that are used toprompt the completion or provision of follow-up member data by automatedcommunications and recommendations. Such templates may be consideredpathways or anticipated events in the lives of members, and may beassociated with milestones or timelines depending upon the nature andfocus on the particular template. It should be noted that at least sometemplates may be “dynamic”, that is, they may be changed over time, suchas to update them with the most current information, practices, orapproaches in the field so that members may benefit from ongoingchanges, developments, technologies, and discoveries.

In an example approach, for example, templates are stored in the systemor database for anticipated events or information in a patient healthjourney. The processing circuitry automatically and without humanintervention sends follow-up de-identified communications to specificcontributing members to prompt contribution of follow-up or missingmember-specific contributed data based upon the template. For example,each template may include anticipated events in a different patienthealth journey. The templates may be based upon analysis of theaggregated member-specific contributed data indicative of events ofother contributing members on the same patient health journey, such ascontributed data from members who may have a similar condition orsymptoms.

Moreover, the templates and corresponding follow-up communications maybe implemented for particular contributing members based upon initiatingevents that are unique to the particular patient health journey andtemplate. Such initiating events may include, for example, a birth, theoccurrence of new symptoms or conditions indicated by thepatient-specific contributed data, a diagnosis indicated by thepatient-specific contributed data, a treatment plan indicated by thepatient-specific contributed data, identification of a physicalcondition potentially detectable from the aggregated member-specificcontributed data, identification of a new treatment of a physicalcondition detectable from the aggregated member-specific contributeddata, or identification of a new examination or test useful indetermining a physical condition detectable from the aggregatedmember-specific contributed data.

The templates may be adapted, changed, or added over time, and may giverise to different standard or member-adapted reports, surveys, andrecommendations. For example, the follow-up communications based uponthe templates may include a custom report adapted to facilitate acontributing member consulting a medical professional, orrecommendations for acquisition of additional data of the respectivecontributing members. Where the member-specific contributed datacomprises health-related data, the recommendations may comprise at leastone physical examination or test related to physical condition of therespective contributing members. Here again, the processing circuitrymay analyze the member-specific contributed data of each member todetermine at least one most convenient and/or cost effective source forthe acquisition of additional data, and the recommendations may includean indication of the most convenient and/or cost effective source foreach respective recommendation to each respective contributing member.

As with other occurrences of contributed data from members, theprocessing circuitry may re-evaluates the member-specific value afterreceipt of follow-up member-specific contributed data from eachrespective contributing member. In such cases, the follow-upcommunications may comprise an indication to each contributing member ofthe re-evaluation of the member-specific value applicable when therespective contributing member contributes the follow-up member-specificcontributed data.

As noted above, a particularly powerful use case for the platform, andthe member-specific contributed data and aggregated data is thedetermination of correlations between and among members sharing the sameor similar characteristics. Such groupings, referred to in thisdisclosure as “cohorts” may be determined based upon one or many factorsdetectable in the contributed and aggregated data, such as a physicalcondition, a diagnosis, a family situation, a predisposition, a patientjourney, and so forth. But many other factors and bases, data types andcombinations of data may be considered for determining cohorts, such asany of those discussed above (e.g., demographic data, personal data,geographic data, omic data, employment data, health history, lifestyle,habits, interests, etc.)

FIG. 15 is a flow chart illustrating an example process for analyzingmember data to provide enhanced value by determining cohorts amongmembers for possible data sharing and follow-up. The cohort process 352may be initiated by any suitable actor, including by contributingmembers 354, the administrative entity 356, by automatic operation(e.g., programming) of the system (e.g., processing circuitry andanalysis engine) 358, by research by interested third-parties 360 (e.g.,health and medical institutions and companies, pharmaceutical firms,etc. contracting with the administrative entity), and so forth. Theinitiation of cohort identification launch execution of analysisroutines that are pre-established or that may be at least partiallydefined by the initiating entity (e.g., based upon a query or searchsubmitted via an online tool). In the illustration, several suchsearches are summarized, including a correlation study 362 to determinemembers who may exhibit some statistical correlation to search criteria,self-discovery 364, which may be considered a more “free-form” analysisfor any potentially meaningful correlations, and cohort clustering 366,which may use any of various clustering algorithms to group members onthe basis of particular characteristics or combinations ofcharacteristics.

The analysis may ultimately result in a listing of possible members ofone or more cohorts. The listing may be qualified, such as by theconfidence level or strength of correlations of the member-specificcontributed data to the criteria of the cohort. In keeping with the datacompletion and correction aspects of the present disclosure, in manycases, this confidence level may be greatly enhanced by identifyingadditional data that could be contributed by candidate members to acohort, and soliciting the members to consider adding such details, suchas by surveys, tests, exams, and analyses. At operation 370, such gapsor desired data may be determined. As before, these determinations mayinclude determination of member-specific recommendations, including, forexample, the suggested data, tests, and exams, as well as where and howto go about acquiring the data, having tests and exams performed, themost cost-effective manner of proceeding, and so forth.

At operation 372 de-identified communications may be sent to thecandidate members. Based upon responses, then, the cohort may benarrowed (or in some case expanded), as noted at operation 374. Thisnarrowing (or expansion) may be based upon improved statisticalcorrelations or confidence levels resulting from the completion ofcontributed data by members. Narrowing is valuable due to the fact thathistorically diseases or conditions have been defined based on theclassification of symptoms or analytical tests. Often the molecularbasis could not be ascertained. As a results a single disease orcondition classified by historic methods may, and often has, multipledifferent molecular causes. Each individual molecular basis may requirean individual treatment to address or resolve the disease or condition.The fact that one disease may have multiple causes at the molecularlevel can confound attempts to identify correlations between a diseaseand genomic signatures. Increasing statistical power can be achieved forinstance by increasing the depth, breadth, and scale of the study or bynarrowing the cohorts in an attempt to reduce the number of differentmolecular causes in a single cohort.

In some cases the members of cohorts may desire to communicate with oneanother, or even with the administrative entity or third-parties (e.g.,research institutions). This is possible, as indicated by operation 376.In general, and again in keeping with the member confidentiality valuesof the platform, such communications and revealing of an individualmember identification is strictly at the option and control of theparticular members. Similarly, it should be noted that at various stagesof cohort determination, analysis, and communication, the members mayopt out of consideration and notification, as indicated by reference 378in FIG. 15 .

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

1. A system comprising: a database maintained by an administrativeentity that, in operation, stores and aggregates member-specificcontributed data transmitted by contributing members withmember-specific contributed data contributed by other contributingmembers, wherein the database comprises an immutable and/orcryptographically encoded ledger and/or a blockchain; and processingcircuitry maintained by the administrative entity that, in operation,executes stored instructions to process member-specific account datareceived from the contributing members via interface pages to establishmember-specific accounts based on the member-specific account data, andattributes a member-specific value to the member-specific accounts basedupon respective member-specific contributed data; wherein the processingcircuitry analyzes the member-specific contributed data for eachcontributing member to determine missing or incorrect data, and sends anautomated de-identified communication to the respective contributingmember by the administrative entity to provide missing data or tocorrect incorrect data, and wherein an identity of the respectivecontributing member to whom the automated de-identified communication issent is not available to the administrative entity originating theautomated de-identified communication.
 2. The system of claim 1, whereinthe processing circuitry determines a quality score based upon acompleteness and/or correctness of the member-specific contributed data.3. The system of claim 2, wherein the processing circuitry is configuredto alter the quality score based upon later contributed data.
 4. Thesystem of claim 2, wherein the member-specific value is based at leastpartially upon the quality score.
 5. The system of claim 1, wherein theprocessing circuitry automatically and without human interventionattempts to complete missing data and/or to correct incorrect data priorto communicating with the respective contributing member.
 6. The systemof claim 1, wherein the database is configured to store member-specificcontributed data of different types, and the processing circuitrydetermines missing or incorrect data of one type based upon analysis ofdata of a different type.
 7. The system of claim 6, wherein the types ofmember-specific contributed data comprise at least two of omic data,phenotype data, health data, personal data, familial data andenvironmental data.
 8. The system of claim 1, wherein the database isconfigured to store member-specific contributed data of different types,and the processing circuitry determines missing or incorrect data of onetype based upon analysis of the same type of data but contributed atdifferent times by the same contributing member.
 9. The system of claim1, wherein the missing or incorrect data is determined based uponanalysis of aggregated data of a plurality of contributing members. 10.The system of claim 1, wherein the missing or incorrect data comprisesat least two of personal data, medical record data, dietary data andwearable device data.
 11. The system of claim 1, wherein the automatedde-identified communication comprises a customized survey based upondata determined to be missing and/or incorrect.
 12. The system of claim1, wherein the automated de-identified communication comprises arecommendation for acquisition of additional data by the respectivecontributing member.
 13. The system of claim 1, wherein the processingcircuitry transfers an asset amount to each member-specific accountbased upon the member-specific value.
 14. The system of claim 1, whereinthe member-specific value is attributed as a currency and/or acryptocurrency and/or an ownership share in the database or databasemaintaining entity.
 15. The system of claim 1, wherein the processingcircuitry is configured to make ledger entries in an immutable and/orcryptographically encoded ledger and/or a blockchain based uponinteraction with the contributing members.
 16. A system comprising: adatabase maintained by an administrative entity that, in operation,stores and aggregates member-specific contributed data transmitted bycontributing members with member-specific contributed data contributedby other contributing members, wherein the database comprises animmutable and/or cryptographically encoded ledger and/or a blockchain;and processing circuitry maintained by the administrative entity that,in operation, executes stored instructions to process member-specificaccount data received from the contributing members via interface pagesto establish member-specific accounts based on the member-specificaccount data, and attributes a member-specific value to themember-specific accounts based upon respective member-specificcontributed data; wherein the processing circuitry analyzes themember-specific contributed data for each contributing member todetermine missing or incorrect data based upon analysis of the same typeof data but contributed at different times by the same contributingmember or based upon analysis of data of a different type, and sends anautomated de-identified communication to the respective contributingmember to provide missing data or to correct incorrect data; and whereinan identity of the respective contributing member to whom the automatedde-identified communication is sent is not available to theadministrative entity originating the automated de-identifiedcommunication.
 17. The system of claim 16, wherein the automatedde-identified communication comprises a customized survey based upondata determined to be missing and/or incorrect.
 18. The system of claim16, wherein the automated de-identified communication comprises arecommendation for acquisition of additional data by the respectivecontributing member.
 19. A system comprising: a database maintained byan administrative entity that, in operation, stores and aggregatesmember-specific contributed data transmitted by contributing memberswith member-specific contributed data contributed by other contributingmembers, wherein the database comprises an immutable and/orcryptographically encoded ledger and/or a blockchain; and processingcircuitry maintained by the administrative entity that, in operation,executes stored instructions to process member-specific account datareceived from the contributing members via interface pages to establishmember-specific accounts based on the member-specific account data, andattributes a member-specific value to the member-specific accounts basedupon respective member-specific contributed data; wherein the processingcircuitry analyzes the member-specific contributed data for acontributing member to determine missing or incorrect data, and sends acustomized survey via an automated de-identified communication to therespective contributing member based upon data determined to be missingand/or incorrect; and wherein an identity of the respective contributingmember to whom the automated de-identified communication is sent is notavailable to the administrative entity originating the automatedde-identified communication.
 20. The system of claim 19, wherein theprocessing circuitry sends a recommendation for acquisition ofadditional data by the contributing member.